00:23 · repliedTask: run a zero-shot metric-depth test on the 12 probe clips (lead/follow ordering, cut-in gap, scale sanity, temporal consistency), code in `src/faultlab/depth.py`, outputs in `runs/depth/`.
00:23 · repliedHF is reachable; the vlm2 venv lacks transformers. Creating a dedicated `.venv-cv` in this repo rather than touching vlm2's environment.
00:23 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
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Your directive: You are Fork F. Build "variant C": existing deterministic CV modules produce the same modifier facts the VLM was asked in M1, in the same consensus file shape, so they can be scored with the existing `score_m1` next to variants A and B. No pod; Mac only. Do not modify data/. Code in src/faultlab/m1_cv.py (+ small adapters if needed), outputs runs/m1/C/<clip>.json, then run `python -m faultlab.score_m1 --variant C` and make the paired table include C (extend score_m1 minimally if its A-vs-B pairing is hardcoded; keep A/B behaviour unchanged).
Consensus file shape to produce (see runs/m1/A/011.json for a real example): {clip, leaf, e0_side, variant:"C", model:"cv", anchor:{t_c, counterpart_track_id}, n_samples:1, n_parsed:1, format_errors:0, questions:[...same Q.to_dict() as m1_questions.question_set(leaf, e0_side, "A")...],…
00:23 · repliedBoth experiments are running: the depth test on the 12 clips, and the CV modules wired in as variant C through the same scorer as the VLM variants. I'll report when they land, with one table per experiment.
00:23 · repliedTask: build variant C (deterministic CV modules → M1 consensus files for 12 clips), score with score_m1, extend the paired table to include C.
00:26 · repliedDepth Pro runs on MPS at 3.7 s per frame and estimates a plausible focal length of about 1019 px. Gold position-order labels exist for 11 clips. Writing the test module now.
00:30 · repliedTask: build variant C (deterministic CV modules → M1 consensus files), score it next to A/B. Done.
**Files**
- `src/faultlab/m1_cv.py` — runner (`PYTHONPATH=src ~/projects/lane-detection/.venv/bin/python -m faultlab.m1_cv`); imports `knia.perception.lights` and `scene` from new-knia, UFLDv2 + a copy of attrs.py's `probe/decode` from lane-detection (that file is a script, not importable), vlm2 `motion.json`/`tracks.json` from data/percept.
- `src/faultlab/score_m1.py` — `--variant C` accepted; `_paired()` takes names; C pairs against both A and B. A/B output unchanged.
- `runs/m1/C/<clip>.json` × 12, `scores.{json,md}`, `README.md` (slot↔module map, gates, tables).
**Scores (gold-known keys, 12 clips)**
| variant | correct | wrong | unknown | no_gold |
|---|---|---|---|---|
| A | 10 | 2 | 13 | 17 |
| B | 7 | 7 | 11 | 17 |
| C | 4 | 2 | 19 | 17 |
Paired: A→C unknown→correct 3 · correct→unknown 9; B→C wrong→correct 3 · correct→wrong 1. Clip-level 2/2/8 vs A, 2/3/7 vs B → no difference.
**Per slot (the useful reading)**
- Turn signal (other), lights.py periodogram: 023, 035 → not_used, **2/2 correct**; the VLM (B) got all three 43-2 clips wrong. 042 refused (L/R both 1.2 Hz, anti-phase; gold = left indicator on) — a real indicator plus a reflection on the other lamp; relaxing the "different sides blinking → refuse" rule to "phase ≈ π → one-sided indicator" might recover it, untested. 011: counterpart appears 0.23 s before contact → 4 frames, gated.
- Lane line type: 035 dashed correct (duty 0.10); 011/042 no paint contrast → unknown; 023 counterpart out of frame at t_c → boundary undetermined. All 12 gold are dashed, so this slot has **no negative example**; only note is that B's two "solid" errors don't occur.
- Ego stopped (flow): 011 wrong (moving vs stopped). Farne…
00:30 · asked<task-notification>
<task-id>a3e7cd8c6bc3852f5</task-id>
<tool-use-id>toolu_01AvDj9GuLVbDRdy8rwVnAxW</tool-use-id>
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<status>completed</status>
<summary>Agent "Variant C: CV modules as M1 facts" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: build variant C (deterministic CV modules → M1 consensus files), score it next to A/B. Done.
**Files**
- `src/faultlab/m1_cv.py` — runner (`PYTHONPATH=src ~/projects/lane-detection/.venv/bin/python -m faultlab.m1_cv`); imports `knia.perception.lights` and `scene` from new-knia, UFLDv2 + a copy of attrs.py's `probe/decode` from lane-detection (that file is a script, not importable), vlm2 `motion.json`/`tracks.json` from data/percept.
- `src/faultlab/score_m1.py` — `--variant C` accepted; `_paired()` takes names; C pairs against both A and B. A/B output unchanged.
- `runs/m1/C/<clip>.json` × 12, `scores.{json,md}`, `README.md` (slot↔module map, gates, tables).
**Scores (gold-known keys, 12 clips)**
| variant | correct | wrong | unknown | no_gold |
|---|---|---|---|---|
| A | 10 | 2 | 13 | 17 |
| B | 7 | 7 | 11 | 17 |
| C | 4 | 2 | 19 | 17 |
Paired: A→C unknown→correct 3 · correct→unknown 9; B→C wrong→correct 3 · correct→wrong 1. Clip-level 2/2/8 vs A, 2/3/7 vs B → no difference.
**Per slot (the useful reading)**
- Turn signal (other), lights.py periodogram: 023, 035 → not_used, **2/2 correct**; the VLM (B) got all three 43-2 clips wrong. 042 refused (L/R both 1.2 Hz, anti-phase; gold…
00:30 · repliedVariant C is in. The headline: the periodogram indicator detector got the counterpart's turn signal right on both clips where it had enough frames, the exact slot the VLM was 0 for 3 on, and it refused rather than guessed on the third. Ego stopped-state from raw optical flow failed, because moving traffic contaminates the median flow, so that slot needs a different signal. Waiting on the depth test before writing the combined report.
00:32 · repliedTask: monocular metric-depth test (Depth Pro zero-shot) on the 12 probe clips for lead/follow ordering and cut-in gap. Done; nothing committed.
**Model that ran**: `apple/DepthPro-hf` via transformers 5.16 in a new `.venv-cv` (added to .gitignore; vlm2's venv untouched). MPS, 2.5 s per frame after a 78 s load. Depth Anything / Metric3D not needed. 48 frames, all 12 clips. Code `src/faultlab/depth.py`, outputs `runs/depth/<clip>.json`, `runs/depth/scores.md`, `runs/depth/vis/{011,023,035,042}.png`.
**1. Lead/follow at contact** (gold = q_position_order, 11 clips labelled; scored only where the counterpart has a box in a pre-contact frame, 9 clips):
| rule | correct |
|---|---|
| Depth Pro raw, 4.5 m threshold | 3/9 |
| Depth Pro after per-clip car-width scale correction | 5/9 |
| bbox bottom-row (y2) only, no depth | 6/9 |
Depth adds nothing over the bottom-row baseline for this fact. Misses are the same shape across all rules: other_ahead labelled clips where the counterpart is beside E0 and clipped at the frame edge (003, 035, 058), plus 011's self_ahead where the box is a huge partial car. Three clips (008, 016, 023) have no counterpart box in any of the 4 frames, and 035, 042, 053 only at −1.0 s: the track-identity bottleneck Fork D found, not a depth problem.
**3. Scale sanity — the main finding**: Depth Pro's metric scale is 1.3 to 3.4× too small on these clips (per-clip k from a 1.8 m car-width prior: 1.29–3.39) and incoherent within a clip (IQR/median 0.22–1.43; 0/12 clips under 0.2). Its own focal estimates (718–1577 px, median 1148) are plausible, so the bias is in the depth, not the FOV. Eyeball confirms: in 042 a car that should be ~25 m by pixel width reads 8.3 m. Absolute metres are unusable without per-clip calibration.
**2. Cut-in gap** (counterpart…
00:32 · asked<task-notification>
<task-id>a1aff3159d8aad8e6</task-id>
<tool-use-id>toolu_01RBuonuCqTBrwfmW88jDLX1</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a1aff3159d8aad8e6.output</output-file>
<status>completed</status>
<summary>Agent "Monocular depth test on 12 clips" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: monocular metric-depth test (Depth Pro zero-shot) on the 12 probe clips for lead/follow ordering and cut-in gap. Done; nothing committed.
**Model that ran**: `apple/DepthPro-hf` via transformers 5.16 in a new `.venv-cv` (added to .gitignore; vlm2's venv untouched). MPS, 2.5 s per frame after a 78 s load. Depth Anything / Metric3D not needed. 48 frames, all 12 clips. Code `src/faultlab/depth.py`, outputs `runs/depth/<clip>.json`, `runs/depth/scores.md`, `runs/depth/vis/{011,023,035,042}.png`.
**1. Lead/follow at contact** (gold = q_position_order, 11 clips labelled; scored only where the counterpart has a box in a pre-contact frame, 9 clips):
| rule | correct |
|---|---|
| Depth Pro raw, 4.5 m threshold | 3/9 |
| Depth Pro after per-clip car-width scale correction | 5/9 |
| bbox bottom-row (y2) only, no depth | 6/9 |
Depth adds nothing over the bottom-row baseline for this fact. Misses are the same shape across all rules: other_ahead labelled clips where the counterpart is beside E0 and clipped at the frame edge (003, 035, 058), plus 011's self_ahead where the box is a huge partial car. Three clips (008, 016, 023) have no counterpart box in any of the 4 frames, and 035, 042, 053 only …
00:32 · repliedBoth experiments are done. Writing the record, then the answer.
00:34 · repliedBoth done. Write-up is `docs/fault-lab-v1-2.md`, sent above with the 042 depth map and the variant C slot-to-module table.
**Depth: I was wrong, and the test shows it.** Depth Pro ran fine on all 48 frames, but its metric scale is 1.3 to 3.4 times too small on this footage and inconsistent within a clip. A car that pixel width puts at about 25 m reads 8 m. The focal estimates are plausible, so the error is in the depth itself, not the FOV.
| Lead-follow at contact, 9 scorable clips | Correct |
|---|---|
| Depth Pro raw | 3/9 |
| Depth Pro with per-clip car-width scale fix | 5/9 |
| Bounding-box bottom row, no depth at all | 6/9 |
Ordering is a box-geometry fact. Cut-in gap in car lengths looks plausible on 011 but is monotone toward contact in only 1 of 5 clips because boxes collapse at impact, so single-frame gaps can't be trusted. A temporally consistent video-depth model is the untried next step if that fact matters.
**Variant C: no gain on totals, one clear win on a slot.**
| Variant | Correct | Wrong | Unknown |
|---|---|---|---|
| A, VLM with KB terms | 10 | 2 | 13 |
| B, VLM with physical questions | 7 | 7 | 11 |
| C, deterministic CV | 4 | 2 | 19 |
- The indicator detector from new-knia got the counterpart's turn signal right on both clips with enough frames, and refused on 042 where both lamps blink in anti-phase, which is a real indicator plus a reflection. The VLM was 0 of 3 on the same clips.
- Ego stopped-state from raw optical flow failed: moving traffic in the band contaminates the median. Needs a road-only band or track-relative motion.
- Line type had no negative example, since all 12 gold clips are dashed.
**What this settles for the CV step.** Turn signal goes to the periodogram detector, ordering to box geometry, gap stays human for now, and s…
00:36 · askedhow do you know whether depth pro is correct or not?
00:37 · repliedWe don't, not in absolute terms. Let me be precise about what the claim rests on.
**What the "1.3 to 3.4 times too small" is measured against.** Two things, neither of which is ground truth:
- A car-width prior. For every tracked car, Z should equal f times 1.8 m over the box width in pixels. The f in that formula is Depth Pro's own focal estimate. So the scale error is really "Depth Pro's depth disagrees with Depth Pro's focal under a constant-width assumption." If its focal is off, the ratio moves with it.
- Internal coherence. The correction factor varies by more than 20% across tracks within a clip. That assumption breaks for buses and trucks, and for the degenerate boxes near impact, so some of that spread is our boxes, not the model.
The "25 m car reads 8 m" line is the same pixel-width reasoning, not an independent measurement. There is no GPS, LiDAR or stereo on these clips, and the dash-ruler scale from new-knia was never run on this set.
**What would actually verify it.** The road carries statutory dimensions. Lane dash pitch is 8 m on ordinary roads, 13 m on 자동차전용도로, 20 m on expressways. The depth difference between two consecutive dash starts along the same lane line is a pure difference of Z values, so it tests the scale with no focal involved. Lane width, 3.0 to 3.5 m, is a second check but needs f. A third check is agreement with a second model, Metric3D v2 or UniDepth, since two models sharing a bias is less likely than one being off.
**What stands regardless.** The ordering result doesn't depend on scale. Bounding-box bottom row beat depth at 6 of 9 versus 5 of 9 whether or not the meters are right. And the temporal instability near contact is visible in the depth values themselves.
So the defensible version of yesterday's statement is: Depth Pro'…
02:56 · askedI found out that there is depth anything is newer than depth pro
02:56 · repliedYou're right, and it changes the test. Depth Pro is October 2024. Depth Anything V2 predates it, but Depth Anything 3 shipped in late 2025 with metric variants, and Video Depth Anything from early 2025 is the temporally consistent model I flagged as the obvious next attempt for the gap fact. Both are worth running on the same 48 frames, and this time with the dash-pitch check so both models are judged against road geometry rather than against a car-width prior. Launching that now.
02:57 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork G. Extend the depth test (src/faultlab/depth.py, runs/depth/) with two newer models and an f-free ground-truth check. Mac only (MPS), use .venv-cv (transformers 5.16, Depth Pro already there). Do not modify data/. Time-box ~2 h; report exact blockers.
1. Models: (a) Depth Anything 3 metric variant (ByteDance, Nov 2025 — check HF hub for `depth-anything/DA3*` metric checkpoints or the `depth_anything_3` package; pick the smallest metric model that runs on MPS), (b) Video Depth Anything (DepthAnything/Video-Depth-Anything, small, metric if available) run on the 1.5 s window [t_c−1.25, t_c+0.25] at 15 fps so we get temporally consistent depth, (c) keep Depth Pro numbers from runs/depth/<clip>.json for comparison. Same 4 frames (t_c−1.0, −0.5, −0.25, 0) from data/videos/<clip>.mp4, same bbox lower-40% median proto…
02:57 · repliedTask: extend the depth test with Depth Anything 3 and Video Depth Anything, add an f-free dash-pitch ground-truth check, re-score, write `runs/depth/scores_v2.md`.
02:57 · repliedRunning: Depth Anything 3 metric and Video Depth Anything on the same frames as Depth Pro, plus the lane-dash pitch check as focal-free ground truth for all three. I'll report per model whether the meters pass, and whether the video model fixes the near-contact instability.
One side note for later: your global notes now list `ams-1080`, 1,080 dashcam clips with a labeler-chosen chart. That is the widening set we were missing for 차43-2 and 차43-3, once I've read its README caveat about the chart being a pre-sort key.
03:19 · repliedTask: extended the depth test with Depth Anything 3 (metric-large), Video Depth Anything (metric ViT-S), Depth Anything V2 metric-outdoor-S, added an f-free dash-length/pitch ground truth, re-scored. Code `src/faultlab/depth_v2.py` + `depth_vda.py`; outputs `runs/depth/scores_v2.md`, `runs/depth/v2/<clip>.json`, `runs/depth/v2/maps/` (251 float16 maps), `runs/depth/vis/{011,023,035,042}_{dp,da3,vda,dav2}.png`. Nothing under `data/` touched.
**Models that ran (MPS, per frame)**: Depth Pro 2.45 s · DA3 metric-L 0.17 s · VDA metric-S 0.31 s (23-frame window in 7 s) · DAv2 metric-outdoor-S 0.09 s. DA3's metric checkpoint outputs *canonical* depth; metres = canonical × f/300 at its 504-px processing width, so it needs a focal — I used Depth Pro's per-frame f. `depth-anything-3` had to be installed `--no-deps` (its `xformers` pin fails to build on Mac) plus omegaconf/moviepy==1.0.3/plyfile/trimesh/typer/pycolmap/evo/e3nn/addict, and needs `KMP_DUPLICATE_LIB_OK=TRUE` (pycolmap brings a second libomp). VDA runs from a git clone at `VDA_ROOT` (scratchpad), not vendored.
**Ground truth**: the automatic lane fit (paint mask + Hough) was rejected — on 011 it locked onto the bonnet edge and car reflections; its §2/§3 tables are kept but marked 무효. I hand-picked dash endpoints from gridded frames (`runs/depth/v2/dash_manual.json`, `vis/grid/`). Only 006 and 053 have identifiable *consecutive* standard dashes; 011 and 035 contribute one dash each. So the robust check is dash **length** (ΔZ near→far / 3 m, no need to identify the next dash), n=6 at conf high/med; pitch pairs n=3 at low/med conf.
**Does each model's metre scale pass (k_len 0.8–1.25)?**
| model | k_len median | pass | reading |
|---|---|---|---|
| Depth Pro | 0.38 | 0/6 | ~2.6× too small — confirms v1's car-width fin…
03:19 · asked<task-notification>
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<tool-use-id>toolu_01AJegcMyWa4qES3F17FKdRU</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/af0f561a5c607f05a.output</output-file>
<status>completed</status>
<summary>Agent "DA3 + video depth with dash-pitch ground truth" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: extended the depth test with Depth Anything 3 (metric-large), Video Depth Anything (metric ViT-S), Depth Anything V2 metric-outdoor-S, added an f-free dash-length/pitch ground truth, re-scored. Code `src/faultlab/depth_v2.py` + `depth_vda.py`; outputs `runs/depth/scores_v2.md`, `runs/depth/v2/<clip>.json`, `runs/depth/v2/maps/` (251 float16 maps), `runs/depth/vis/{011,023,035,042}_{dp,da3,vda,dav2}.png`. Nothing under `data/` touched.
**Models that ran (MPS, per frame)**: Depth Pro 2.45 s · DA3 metric-L 0.17 s · VDA metric-S 0.31 s (23-frame window in 7 s) · DAv2 metric-outdoor-S 0.09 s. DA3's metric checkpoint outputs *canonical* depth; metres = canonical × f/300 at its 504-px processing width, so it needs a focal — I used Depth Pro's per-frame f. `depth-anything-3` had to be installed `--no-deps` (its `xformers` pin fails to build on Mac) plus omegaconf/moviepy==1.0.3/plyfile/trimesh/typer/pycolmap/evo/e3nn/addict, and needs `KMP_DUPLICATE_LIB_OK=TRUE` (pycolmap brings a second libomp). VDA runs from a git clone at `VDA_ROOT` (scratchpad), not vendored.
**Ground truth**: the automatic lane fit (paint mask + Hough) was rejected — on 011 it locked onto the bonnet edge and…
03:19 · repliedResults are in, and they answer your question about Depth Pro with a focal-free check.
03:19 · repliedFour models now, judged against painted dash length, which uses only depth differences so no focal is involved.
| Model | Scale factor, median | Passes 0.8 to 1.25 | Reading |
|---|---|---|---|
| Depth Pro | 0.38 | 0/6 | About 2.6× too small. The v1 finding holds by an independent route |
| Depth Anything 3 metric-L | 0.92 | 1/6 | Centred right, but 0.53 to 1.69 across clips |
| Video Depth Anything metric-S | 2.47 | 1/6 | About 2.5× too large |
| Depth Anything V2 metric-outdoor-S | 2.36 | 1/6 | About 2.3× too large |
So your instinct about Depth Pro was right in one sense: it is the worst of the four on this footage. But no two models agree with each other on the same tracks, ratios run from 1.4× to 7×, and only one clip per model is internally coherent. Depth Anything 3 is the one candidate: its canonical depth lands near 1.0 when given a decent focal length, and it runs at 0.17 s a frame versus 2.45 for Depth Pro. What it needs is a focal source per clip. Dash pitch could provide it, but the automatic lane fit latched onto the bonnet edge and reflections and had to be rejected, so the ground truth here was hand-picked on six clips.
Two things from the earlier test held up. Lead-follow is a box-geometry fact: the two models that scored 8 of 9 did so only because their inflated meters push everything past the threshold into the majority class, and they fall back to 3 to 5 of 9 once corrected. And the video model is the most temporally stable near contact, 3 of 5 clips monotone, but three clips still have no counterpart box at all before impact.
Net for the CV step: no depth model goes in yet. If the cut-in gap becomes a must-have fact, the path is DA3 plus a per-clip focal from lane geometry, which means the lane detector has to be fixed first. Everything is recor…
05:52 · askedthe problem with lane detection is reflection? we are building the pipeline on ~/projects/cv which has a hood removal gate with segmentation. would this help? are there other lane detection problems?
05:52 · repliedLet me look at what the hood gate in `~/projects/cv` actually does before answering, since the reflection failure I reported was in my own quick Hough fit, not in the real detector.
05:53 · repliedYes, partly, and I should be precise about which failure was whose. The reflection and bonnet-edge failure was in my own quick Hough fit for the dash check. UFLDv2, the detector in cv-processing and lane-detection, has different problems. Your ego mask helps with a specific and real class of errors, but it isn't the main reason lanes fail on 43% of clips.
**What the hood gate fixes**
- Bonnet edge read as a lane line, and dashboard or mount reflections read as paint. Both sit exactly inside the Mask2Former ego mask, so painting the mask before any lane, depth or flow step removes them at the source.
- The same mask fixes the ego-stopped failure from variant C: the optical-flow band was contaminated by traffic and by the hood. Mask the ego region and restrict the band to road pixels, and stopped-versus-moving becomes measurable again.
- Depth models also hallucinate in the hood region. Masking it improves the dash sampling and the near-field scale.
**The other lane problems, ranked by how much they cost us**
1. **Detector success rate.** UFLDv2 gets a usable lane model on 57% of clips. Night, rain, worn paint and headlight glare account for most of the rest. A mask does nothing here.
2. **Fit-off-paint.** When it does fit, the model slides onto curbs, rails and shadows. new-knia's paint-support gate lifted corridor accuracy from 56.8% to 79.7%, and their lane-align study rejected every hypothesis for fixing it further.
3. **Occlusion at the moment we care about.** The counterpart covers the very line it is crossing at contact. That is why line type came back unknown on 011 and 042.
4. **Model design.** CULane row anchors, four-lane cap, trained on Chinese roads. Wide roads, merges and curves break it, and lane index flickers three or more times on 34% of tracks befor…
05:59 · askeddon't contaminate the ~/projects/cv project, but try what we can on here. and do check once more if there are better models (please lift off the license problems - this is feasibility test not production implementation) for lanes. and another question: does this mean depth anything 3 outputs camera intrinsics/extrinsics? if other models use or ouput one, can't we use those information down the line or adjust?
06:00 · repliedOn the intrinsics question, short answer first, then I'll launch both trials here without touching `~/projects/cv`.
**Yes, several of these models output camera parameters, and that is the lever.** Depth Pro has a focal-length head, which is where the f I used came from. Depth Anything 3 predicts a depth-plus-ray representation from which intrinsics are recovered, and in multi-view mode it estimates poses. UniDepth v2 and MoGe-2 also output intrinsics. GeoCalib, already in your lane-detection project, outputs focal, pitch, roll and radial distortion with uncertainties. Extrinsics relative to the road, meaning pitch, roll and camera height, come from the horizon and lane geometry rather than from any depth model.
The point that matters: the camera is fixed for the whole clip, so all these estimates can be pooled across frames and across models into one robust per-clip focal and horizon, and then used two ways. First, to scale Depth Anything 3's canonical depth, which was the one model centred near 1.0. Second, to run new-knia's flat-road geometry, which currently needs a lane fit to calibrate and therefore only yields on 56% of clips. Whether it works hinges on one thing I haven't measured: do the focal estimates agree with each other within a clip. Depth Pro's depth was 2.6× off while its focal looked plausible, and GeoCalib's focal uncertainty is 500 to 900 px, so agreement is not a given. That is exactly what the second trial measures.
06:00 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork H. Two jobs, both inside /Users/liwimoi/projects/fault-lab only. HARD RULE: do not write, modify, install into, or import code from ~/projects/cv (you may read its config.toml/CLAUDE.md for reference; reimplement anything you need here). Do not modify data/. Mac only (MPS), use .venv-cv (transformers 5.16, torch, DA3 already installed) and add packages there with uv as needed. Licence is irrelevant for this feasibility test: pick the best-performing models regardless of licence and do not caveat on licence.
Job 1 — survey (WebSearch/WebFetch, ~30 min): what are the best lane-line detection and road-marking segmentation models as of 2024–2026 that (a) have downloadable weights and (b) can run on a Mac with MPS or CPU in reasonable time? Cover at least: CLRerNet / CLRNet / CondLaneNet / GANet / LaneATT (CULane S…
06:00 · repliedTask: survey best lane/marking models (2024–26, weights available, Mac-runnable) and run ego-mask + Vistas marking + lane-detector trials on the 12 probe clips inside fault-lab only.
06:00 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork I. Test whether a per-clip camera model (focal, horizon/pitch, roll) can be recovered by pooling the intrinsics that depth/calibration models output, and whether it fixes the depth scale. Work only inside /Users/liwimoi/projects/fault-lab; do not write to ~/projects/cv or ~/projects/lane-detection (reading them is fine). Do not modify data/. Mac only, .venv-cv (Depth Pro, DA3, VDA, DAv2 set up by Fork G — see src/faultlab/depth_v2.py and runs/depth/v2/ for how they were run and the cached maps). Licence irrelevant.
1. Focal sources per clip, on the same 4 frames per clip used before (t_c−1.0, −0.5, −0.25, 0; data/videos/<clip>.mp4): (a) Depth Pro FOV head — already in runs/depth/<clip>.json per frame; (b) GeoCalib (weights/usage in ~/projects/lane-detection/work/survey.py; install `geocalib` into .venv-cv if n…
06:01 · repliedTask: recover a per-clip camera model (f, y_h, roll) by pooling Depth Pro / GeoCalib / DA3 / MoGe-2 intrinsics, then test whether a fused f makes DA3 metres pass the dash-length check.
06:01 · repliedTwo trials are running here in fault-lab, nothing written to `~/projects/cv`:
- **Lanes**: a licence-agnostic survey of current lane and road-marking models, then trials on the 12 clips. Ego mask reimplemented from the same Mapillary Vistas segmenter, its lane-marking pixels as the paint source for the fit and the dash sampling, one current CULane-family detector against UFLDv2 with and without the mask, and solid-versus-dashed from the marking mask.
- **Intrinsics**: focal and horizon pooled from Depth Pro, GeoCalib, Depth Anything 3, and MoGe-2 or UniDepth, checked for agreement within each clip, then used to rescale DA3 and rerun the dash-length check, plus a flat-road cross-check.
I'll report both with tables when they land.
06:04 · repliedGeoCalib finished on all 12 clips. While DA3-large and MoGe download, I'll write the fusion step.
06:07 · repliedThree sources fused so far. MoGe fails on MPS autocast in both precisions, so I'll try the model fully in half precision before falling back to CPU.
06:11 · repliedTask: recover a per-clip camera model by pooling Depth Pro / GeoCalib / DA3 / MoGe-2 intrinsics and test whether a fused f makes DA3 metres pass the dash check. Done; report `runs/depth/intrinsics.md`, code `src/faultlab/intrinsics.py` + `intrinsics_fuse.py`, sources in `runs/depth/intrinsics/*.json` (+ `fused.json`, MoGe maps). Nothing committed, nothing outside fault-lab written, data/ untouched.
**Sources that ran (12 clips × 4 frames, MPS)**: Depth Pro FOV head (cached) · GeoCalib via lane-detection's venv (read-only use) · DA3 any-view `da3-large` camera head — the **metric checkpoint returns `intrinsics=None`**, only the any-view one has the camera decoder · MoGe-2 `moge-2-vitl-normal` (runs on MPS only with the whole model `.half()`; both autocast paths fail).
**1. Is f recoverable? No, not beyond ±25%.**
| source | f / f_fused, median [clip IQR] | within-clip noise (4 frames) |
|---|---|---|
| Depth Pro | 0.80 [0.24] | 0.06 |
| GeoCalib | 0.91 [0.13] | 0.06 |
| MoGe-2 | 1.03 [0.04] | 0.04 |
| DA3 any-view | 1.37 [0.14] | 0.02 |
Within-source noise is small; the disagreement is systematic bias per source. All four within ±15% of the fused value: **0/12** clips; ≥2 of ≥3 sources within: 8/12. DA3's camera head returns ≈2050 px on 8/12 clips regardless of content — a prior, not a measurement, so its "stability" is empty. Most trustworthy single source by consistency is MoGe-2, with no external proof it is right.
**2. Is the horizon recoverable? Yes.** GeoCalib pitch (sign convention y_h = H/2 + f·tan(pitch) — the other sign matched 1/12) and an independent flat-road fit (y2 = y_h + fH/Z over DA3 depth × bbox bottoms) agree: |Δy_h| median **18 px**, ≤30 px on **8/12**, ≤50 px on 11/12. Roll |·| < 4° everywhere.
**3. Does fused f make DA3 pass the dash check? N…
06:11 · asked<task-notification>
<task-id>a4183dd2b565e03b2</task-id>
<tool-use-id>toolu_019SxeCVB4pH36bssP1F86mU</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a4183dd2b565e03b2.output</output-file>
<status>completed</status>
<summary>Agent "Per-clip camera intrinsics fusion test" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: recover a per-clip camera model by pooling Depth Pro / GeoCalib / DA3 / MoGe-2 intrinsics and test whether a fused f makes DA3 metres pass the dash check. Done; report `runs/depth/intrinsics.md`, code `src/faultlab/intrinsics.py` + `intrinsics_fuse.py`, sources in `runs/depth/intrinsics/*.json` (+ `fused.json`, MoGe maps). Nothing committed, nothing outside fault-lab written, data/ untouched.
**Sources that ran (12 clips × 4 frames, MPS)**: Depth Pro FOV head (cached) · GeoCalib via lane-detection's venv (read-only use) · DA3 any-view `da3-large` camera head — the **metric checkpoint returns `intrinsics=None`**, only the any-view one has the camera decoder · MoGe-2 `moge-2-vitl-normal` (runs on MPS only with the whole model `.half()`; both autocast paths fail).
**1. Is f recoverable? No, not beyond ±25%.**
| source | f / f_fused, median [clip IQR] | within-clip noise (4 frames) |
|---|---|---|
| Depth Pro | 0.80 [0.24] | 0.06 |
| GeoCalib | 0.91 [0.13] | 0.06 |
| MoGe-2 | 1.03 [0.04] | 0.04 |
| DA3 any-view | 1.37 [0.14] | 0.02 |
Within-source noise is small; the disagreement is systematic bias per source. All four within ±15% of the fused value: **0/12** clips; ≥2 of ≥3 sources with…
06:11 · repliedThe intrinsics trial is back, and it answers your question cleanly: pooling the models recovers camera **orientation** but not **scale**. Horizon, pitch and roll agree across independent routes to within about 18 px. Focal length does not: the four sources carry systematic biases of up to 37% relative to each other, Depth Anything 3's camera head returns about 2050 px on 8 of 12 clips regardless of content, so it is a prior rather than a measurement, and only MoGe-2 is internally consistent with nothing external to prove it right. Rescaling DA3 with a fused focal moves the dash-check pass count between 1 and 3 of 6 depending on which sources are pooled, so the focal uncertainty passes straight into the meters.
So the camera parameters these models emit are usable downstream for geometry that needs the horizon, and not for anything that needs meters until a real metric anchor exists on the road. Waiting on the lane trial, which is the anchor's source.
06:15 · repliedTask: survey lane/marking models and run ego-mask + Vistas-marking + lane-detector trials on the 12 probe clips, inside fault-lab only. Done; nothing committed, nothing under `data/` or `~/projects/cv` touched.
**Survey (full table in `runs/lanes/scores.md` §1)**
- No public Mapillary Vistas **v2.0** (124-class: dashed/solid, hatched 안전지대, bus-lane) checkpoint exists. HF has only the two v1.2 Mask2Formers; OneFormer and InternImage Mapillary weights report 62.9–64.0 mIoU consistent with the 65-class set. Dashed/solid/hatching by class means training one.
- Best CULane geometry with public weights: CLRerNet DLA34 F1 81.11/81.55 (official, mmdet 3.3 + Docker) or UnLanedet's pure-PyTorch CLRerNet/CLRNet 79.2–80.2 — but UnLanedet is "Linux only", torch 2.3.1+cu121 pinned; not run on the Mac. LaneATT/BézierLaneNet (75.6–75.8) offer nothing over UFLDv2 (76.0).
- Mac-runnable and run here: Mask2Former Vistas v1.2 (ego + marking pixels, ~1.5 s/frame), YOLOPv2 TorchScript (lane-line + drivable, 0.3 s/frame, BDD lane IoU 27.2), UFLDv2 res34. TwinLiteNet+ (lane IoU 34.2, pure PyTorch) is the cheaper YOLOPv2 replacement, not run. VPGNet: Caffe only. ApolloScape: no maintained weights. Grounded-SAM-2: untested, the only route to 안전지대 without training.
**Trials (12 clips, t_c−1.0 and −0.5; n/N)**
| what | result |
|---|---|
| Ego mask (Mask2Former, Ego Vehicle + Car Mount, 5-frame vote) | hood found 11/12 (058 has no visible hood) |
| UFLDv2 raw → ego-masked | found 10/12 → 10/12 · ego pair 9/12 → 9/12 · pair on Vistas paint (both ≥0.3) 3/12 → 3/12 |
| YOLOPv2 raw → masked | lane pixels 12/12 · ego pair 7/12 → 8/12 · agreement with Vistas paint 0.33–1.00 (median ≈0.6) |
| Vistas marking pixels | present on 12/12 frames (0.2–2.4 %), including 011 where UFLDv2 finds nothing |
| Stra…
06:15 · asked<task-notification>
<task-id>ac0c4b960e6903af0</task-id>
<tool-use-id>toolu_016WXxzYEimWTDMjjgmodh88</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/ac0c4b960e6903af0.output</output-file>
<status>completed</status>
<summary>Agent "Lane model survey + trials in fault-lab" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: survey lane/marking models and run ego-mask + Vistas-marking + lane-detector trials on the 12 probe clips, inside fault-lab only. Done; nothing committed, nothing under `data/` or `~/projects/cv` touched.
**Survey (full table in `runs/lanes/scores.md` §1)**
- No public Mapillary Vistas **v2.0** (124-class: dashed/solid, hatched 안전지대, bus-lane) checkpoint exists. HF has only the two v1.2 Mask2Formers; OneFormer and InternImage Mapillary weights report 62.9–64.0 mIoU consistent with the 65-class set. Dashed/solid/hatching by class means training one.
- Best CULane geometry with public weights: CLRerNet DLA34 F1 81.11/81.55 (official, mmdet 3.3 + Docker) or UnLanedet's pure-PyTorch CLRerNet/CLRNet 79.2–80.2 — but UnLanedet is "Linux only", torch 2.3.1+cu121 pinned; not run on the Mac. LaneATT/BézierLaneNet (75.6–75.8) offer nothing over UFLDv2 (76.0).
- Mac-runnable and run here: Mask2Former Vistas v1.2 (ego + marking pixels, ~1.5 s/frame), YOLOPv2 TorchScript (lane-line + drivable, 0.3 s/frame, BDD lane IoU 27.2), UFLDv2 res34. TwinLiteNet+ (lane IoU 34.2, pure PyTorch) is the cheaper YOLOPv2 replacement, not run. VPGNet: Caffe only. ApolloScape: no maintained weights. Grounded-SAM-2: un…
06:16 · repliedBoth trials are done and recorded in `docs/fault-lab-v1-3.md`, sent above with the two detail reports.
**Hood mask: adopt it, but not for lanes.** Reimplemented here from the same Mapillary Vistas segmenter, it finds the bonnet on 11 of 12 clips. UFLDv2's results are identical with and without it: lanes found 10 of 12, ego pair 9 of 12, ego pair actually sitting on paint 3 of 12. Learned detectors already ignore the hood. The bonnet-and-reflection failure was specific to my quick Hough fit. The mask still earns its place for optical flow, near-field depth and defining the ego region, which is where the stopped-state fix lives.
**The real lane problem is fit-off-paint.** Only 3 of 12 ego-lane pairs lie on the marking pixels. That is new-knia's measured failure showing up again on a different set. The marking pixels themselves are present on all 12 frames, including 011 where UFLDv2 finds nothing, but they are too fat and too lens-curved for a straight fit. The combination to test next is UFLDv2 for geometry with the segmentation pixels validating the line and sampling dashed-versus-solid along it.
**Model survey, licence ignored.** No public checkpoint exists for Mapillary Vistas v2.0, the label set with dashed, solid and hatched classes, so line type and 안전지대 by class means training one or using Grounded-SAM-2 with text prompts. The best CULane geometry with public weights is CLRerNet at F1 81, which needs Linux and CUDA, so it wasn't run here. LaneATT and Bézier variants offer nothing over UFLDv2. False-solid rate on the 12 dashed clips: 0 by the segmentation route, 1 by the duty-cycle route.
**Intrinsics: orientation yes, scale no.** Horizon, pitch and roll from independent routes agree within about 18 px. Focal lengths from the four sources disagree systematicall…
06:36 · askedtry the ufld geometry + vistas paint validation combo
06:37 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork J. Build and score the "UFLDv2 geometry + Mapillary Vistas paint validation" combination on the 12 probe clips, inside /Users/liwimoi/projects/fault-lab only (read ~/projects/lane-detection for how UFLDv2 was run; do not write there or to ~/projects/cv; do not modify data/). Mac, .venv-cv (Fork H set it up: see src/faultlab/lanes_trial.py, egomask.py, runs/lanes/ for the ego masks, Vistas marking masks, UFLDv2 outputs and YOLOPv2). Licence irrelevant. Time-box ~2.5 h.
Goal: turn UFLDv2's lane hypotheses into validated ego-lane lines that lie on real paint, then use those lines to (a) sample dashed/solid along them and (b) find consecutive dash starts as a metric anchor.
Method (be explicit in code and report about each choice):
1. Inputs per clip and frame (t_c−1.0, −0.5, and add −1.5 for more dash runs): UFL…
06:37 · repliedTask: build and score the UFLDv2-geometry + Vistas-paint-validation combo on the 12 probe clips, in fault-lab only.
06:37 · repliedRunning: UFLDv2 lane hypotheses validated and snapped onto skeletonized Vistas paint, refit in the inverse-row domain, then used for the ego pair, dashed-versus-solid, and dash starts with an implied focal. YOLOPv2 is tried as the alternative geometry source under the same validation. I'll report against the 3 of 12 baseline when it lands.
06:45 · repliedThe wait loop exited early on the previous loose log, so that was the stale report. Waiting properly for both reruns.
06:47 · repliedTask: built and scored the UFLDv2-geometry + Vistas-paint-validation combo on the 12 probe clips, fault-lab only. Done; nothing committed, nothing outside fault-lab written, data/ untouched.
**Files**: `src/faultlab/lanes_combo.py` (run with `KMP_DUPLICATE_LIB_OK=TRUE PYTHONPATH=src .venv-cv/bin/python -m faultlab.lanes_combo [--d 10 --sup 0.4 --cov 0.3 --tag _loose]`), `runs/lanes/combo/{scores.md, scores_loose.md, results*.json, vis/<clip>.png, vis/sheet.png}`. scikit-image added to .venv-cv.
**Method as built**: Vistas "Lane Marking - General" on the ego-masked frame → skeletonize → distance map. Per UFLDv2 lane: support = points within d px of skeleton, coverage = anchor rows carrying a point between y_h+40 and the hood (y_h from Fork I's flat-road fit). Accept, snap to nearest skeleton pixel, refit x = a + b·u + c·u² in u = 1/(y−y_h). Ego pair = nearest validated line each side. Dash sampling uniform in u along the curve. Two dash sources: the Vistas mask (as directed) and, added after seeing it fail, an RGB L* ridge (peak in ±hw minus road median outside, >18) — semantic masks bridge the 5 m gaps (≈8 px at the 512-px processing size). Frames −1.5/−1.0/−0.5 plus the manual-dash frames for 011/035. YOLOPv2 skeleton components run through the same validation.
**Results (12 clips)**
| | strict d6·sup.5·cov.4 | loose d10·sup.4·cov.3 | Fork H raw UFLD |
|---|---|---|---|
| validated ego pair, both on paint | 0/12 | 2/12 | 3/12 |
| ≥1 validated line on paint | 3/12 | 8/12 | — |
| YOLOPv2 source: pair / ≥1 | 0/12 · 5/12 | 3/12 · 8/12 | — |
| crossed-line type (gold all dashed) | dashed 1 · false solid 0 · unknown 11 | dashed 2 · false solid 1 · unknown 9 | false solid 0–1 |
| clips with ≥2 dash starts on a validated line | 5/12 | 10/12 | 4 hand-picked |
- **Ego pairs…
06:47 · asked<task-notification>
<task-id>a3eab5660e57c04fa</task-id>
<tool-use-id>toolu_01UE9JYGczWxitt3xQpaceZG</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a3eab5660e57c04fa.output</output-file>
<status>completed</status>
<summary>Agent "UFLD geometry + Vistas paint validation combo" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: built and scored the UFLDv2-geometry + Vistas-paint-validation combo on the 12 probe clips, fault-lab only. Done; nothing committed, nothing outside fault-lab written, data/ untouched.
**Files**: `src/faultlab/lanes_combo.py` (run with `KMP_DUPLICATE_LIB_OK=TRUE PYTHONPATH=src .venv-cv/bin/python -m faultlab.lanes_combo [--d 10 --sup 0.4 --cov 0.3 --tag _loose]`), `runs/lanes/combo/{scores.md, scores_loose.md, results*.json, vis/<clip>.png, vis/sheet.png}`. scikit-image added to .venv-cv.
**Method as built**: Vistas "Lane Marking - General" on the ego-masked frame → skeletonize → distance map. Per UFLDv2 lane: support = points within d px of skeleton, coverage = anchor rows carrying a point between y_h+40 and the hood (y_h from Fork I's flat-road fit). Accept, snap to nearest skeleton pixel, refit x = a + b·u + c·u² in u = 1/(y−y_h). Ego pair = nearest validated line each side. Dash sampling uniform in u along the curve. Two dash sources: the Vistas mask (as directed) and, added after seeing it fail, an RGB L* ridge (peak in ±hw minus road median outside, >18) — semantic masks bridge the 5 m gaps (≈8 px at the 512-px processing size). Frames −1.5/−1.0/−0.5 plus the manu…
06:48 · repliedNegative result, and a clean one. Details in `runs/lanes/combo/scores.md` and the contact sheet, both sent.
| | Strict thresholds | Loose thresholds | Raw UFLDv2 |
|---|---|---|---|
| Validated ego pair, both lines on paint | 0/12 | 2/12 | 3/12 |
| At least one validated line on paint | 3/12 | 8/12 | |
| YOLOPv2 as geometry: pair / one line | 0/12 · 5/12 | 3/12 · 8/12 | |
| Crossed-line type, gold all dashed | 1 dashed, 0 false solid, 11 unknown | 2 dashed, 1 false solid, 9 unknown | |
**The combo is a filter, not a fix.** Most UFLDv2 hypotheses put fewer than half their points within 6 px of real paint, sitting on curbs, dashboard edges and shadows. Validation correctly throws them out but has nothing to put in their place, so the on-paint rate goes down, not up. YOLOPv2 as the geometry source behaves the same.
**Two things did work, narrowly.** Where a line does validate and the dashes are read from an RGB brightness ridge rather than the semantic mask, dash starts match your hand-picked rows within 2 px on clip 006. The semantic mask itself bridges the 5 m gaps and reads most lines as one continuous run, so it can't see dashes. And the one false solid on 035 is a real solid bus-lane boundary on the right, so the type call was correct and the error was choosing which side the counterpart crossed.
**One door closed.** Using dash length to derive a focal for Depth Anything 3 scatters between 254 and 8874 px within clips. A 3 m step at 15 to 40 m is below the depth map's noise floor, so the DA3-canonical route to meters is finished regardless of the lane result.
**Where that leaves lanes.** The next lever is a detector that sits on paint in the first place, CLRerNet on Linux with CUDA, or a marking-specific segmentation trained with dashed, solid and hatched classes…
06:52 · askedrun CLRerNet on the pod on the same 12 clips
06:52 · repliedSetting up a fresh Linux/CUDA pod for this, separate from the shared vLLM one, then handing the run to a fork. First the SSH key so the pod is reachable.
06:53 · askedBase directory for this skill: /Users/liwimoi/.claude/plugins/cache/runpod/runpod/1.2.0/skills/runpod-mcp
# Runpod MCP
The Runpod MCP server exposes Runpod's control plane as structured tool calls,
so an MCP-capable agent can manage infrastructure without shelling out. It is the
same Runpod REST API that `runpodctl` uses — pick MCP when its tools are
connected (typed params, structured errors, no shell quoting).
**For a multi-step job, read the worked example before calling tools.** Tool calls are easy
to issue and easy to issue in the wrong order — the verified end-to-end sequences live in
[runpod/golden-paths/README.md](../runpod/golden-paths/README.md) (image → template →
endpoint, pod → volume → serverless, multi-region, autoscaling, monitoring). This skill
covers what each tool does; the paths cover what order to do them in and what it costs.
## Connect
Connect the hosted server with **your API key as a Bearer header** if you also use runpodctl/flash — that one key auths the MCP *and* the CLIs (the 80% path):
```bash
claude mcp add --transport http runpod -s user https://mcp.getrunpod.io/ \
--header "Authorization: Bearer $RUNPOD_API_KEY"
```
Plain **OAuth** ("Sign in with Runpod", via `npx @runpod/mcp-server@latest add`) is MCP-only — the CLIs stay unauthed, so use it only for MCP-only work. Local **stdio** runs the server as a subprocess with your key. Those variants + the key-vs-OAuth tradeoff: **[reference/connect.md](reference/connect.md)**. After connecting, reconnect the client (in Claude Code, `/mcp`) so the tools load.
**Verify it's live (do this before relying on MCP):** in Claude Code run `/mcp` —
`runpod` should show **Connected**, not *Needs authentication* (if it's the latter,
sign in there first; the bundled plugin server registers the URL …
06:53 · repliedPod is up: RTX 4090 at $0.74/h, id `9yx58lwpqm9use`. Handing the run to a fork, which will terminate the pod when done since I created this one.
06:54 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork K. Run CLRerNet (CULane DLA34 weights, the F1 81 model) on the 12 probe clips on a fresh RunPod GPU pod and score it with the same on-paint metric as the UFLDv2 trials. Work inside /Users/liwimoi/projects/fault-lab locally; do not modify data/; do not touch ~/projects/cv or the other vLLM pod (ao420khj7w3x58, someone else's).
Pod: id `9yx58lwpqm9use`, RTX 4090, image runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404, 22/tcp exposed, your public key is authorised. Get the direct SSH ip/port with the MCP tool `mcp__plugin_runpod_runpod__get-pod` (podId 9yx58lwpqm9use → runtime.ports entry with private 22 → ip + public port; ssh -p <port> root@<ip>). Until runtime is populated, the proxy `ssh 9yx58lwpqm9use-64411ec6@ssh.runpod.io` works for shell (not scp). Poll get-pod every ~30 s up to 5 min. Use `ssh -o StrictHo…
06:54 · repliedCLRerNet run is underway on the new pod: frames extracted locally, model installed and run there, lane points brought back and scored against the same paint-support metric as UFLDv2. The fork terminates the pod when it finishes. I'll report the comparison when it lands.
06:54 · repliedTask: run CLRerNet (CULane DLA34) on the 12 probe clips on pod 9yx58lwpqm9use, score ego-pair-on-paint vs UFLDv2, terminate the pod.
07:10 · repliedBuilds are running on the pod: mmcv is compiling from source against the pod's torch 2.8 (47 compiler processes), and UnLanedet is installing in a separate venv as the fallback. Frames, params and the inference script are all in place. I'll run inference as soon as either import succeeds.
07:10 · asked<task-notification>
<task-id>a935d4ae4fc7cdca1</task-id>
<tool-use-id>toolu_01QcPVced8wVzcaraQbDGgAt</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a935d4ae4fc7cdca1.output</output-file>
<status>completed</status>
<summary>Agent "CLRerNet on pod, 12 clips" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Builds are running on the pod: mmcv is compiling from source against the pod's torch 2.8 (47 compiler processes), and UnLanedet is installing in a separate venv as the fallback. Frames, params and the inference script are all in place. I'll run inference as soon as either import succeeds.</result>
<usage><subagent_tokens>555386</subagent_tokens><tool_uses>191</tool_uses><duration_ms>970572</duration_ms></usage>
</task-notification>
07:10 · repliedThe fork paused with builds still compiling on the pod. Resuming it so it finishes the run and terminates the pod.
07:10 · askedThe coordinator sent a message while you were working:
Continue to completion. Wait for whichever of mmcv or UnLanedet imports first (poll every ~2 min; if mmcv compilation exceeds 25 more minutes, abandon it and use UnLanedet), run inference on the 36 frames, scp the JSON back, score locally against the paint metric, write runs/lanes/clrernet/scores.md and the overlays, then terminate pod 9yx58lwpqm9use with delete-pod and confirm with get-pod. Do not stop again before the pod is terminated; if you hit a hard blocker, terminate the pod first and then report the blocker.
Address this before completing your current task.
07:10 · repliedResumed. It will finish inference, score, and terminate the pod before reporting. Nothing else pending on my side.
07:26 · repliedTask: ran CLRerNet (CULane DLA34-EMA) on the 12 probe clips on pod 9yx58lwpqm9use, scored ego-pair-on-paint vs UFLDv2, terminated the pod. Done.
**Files**: `runs/lanes/clrernet/{scores.md, results.json, raw/clrernet_preds.json, params.json, pod_prep_infer.py, versions.txt, vis/<frame>.png ×38, vis/sheet.png}`, `src/faultlab/lanes_clrernet.py` (scorer). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Result (12 clips, frames t_c−1.5/−1.0/−0.5 + the two manual-dash frames)**
| | CLRerNet mode A (whole frame → 1640×590) | CLRerNet mode B (road band → 1640×320) | UFLDv2 (Fork H, masked) |
|---|---|---|---|
| lanes found on ≥1 frame | 11/12 | 10/12 | 10/12 |
| ego pair found | 10/12 | 10/12 | 11/12 |
| **ego pair on paint, both ≥ 0.5** | 0/12 | **3/12** | **3/12** |
| ego pair on paint, both ≥ 0.3 | 3/12 | 5/12 | 4/12 |
| ego-pair line support6 median | 0.31 | 0.65 | 0.65 |
| crossed-line type (gold all dashed) | — | dashed 5 · false solid 0 · unknown 7 | — |
| manual dash near-edge ≤ 15 px | — | **5/7** (006: 1.9/1.3/0.0 px on three consecutive dashes; 053 12.3; 035 14.7; misses 053-far 58, 011 108) | 3/7 (Fork J combo) |
- **Does CLRerNet's ego pair sit on paint more often than UFLDv2's 3/12? No.** Same 3/12 on the strict metric, 5 vs 4 on the loose one, identical per-line support median. Within "2–3 clips = no difference".
- **Input geometry matters more than the model.** Squashing the full 1080p frame to CULane's shape (mode A) is useless (0/12, lines through the hood). The band crop (mode B) is what makes it work; the overlays show B riding real markings on 003/006/023/027/035/053.
- **Dash anchor**: usable where B validates — 006's three consecutive dashes land within 2 px of the hand-picked rows, so a pitch-based metric anchor is realistic o…
07:26 · asked<task-notification>
<task-id>a935d4ae4fc7cdca1</task-id>
<tool-use-id>toolu_019XqL2kAwm38wNQQn8Fet2o</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a935d4ae4fc7cdca1.output</output-file>
<status>completed</status>
<summary>Agent "CLRerNet on pod, 12 clips" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: ran CLRerNet (CULane DLA34-EMA) on the 12 probe clips on pod 9yx58lwpqm9use, scored ego-pair-on-paint vs UFLDv2, terminated the pod. Done.
**Files**: `runs/lanes/clrernet/{scores.md, results.json, raw/clrernet_preds.json, params.json, pod_prep_infer.py, versions.txt, vis/<frame>.png ×38, vis/sheet.png}`, `src/faultlab/lanes_clrernet.py` (scorer). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Result (12 clips, frames t_c−1.5/−1.0/−0.5 + the two manual-dash frames)**
| | CLRerNet mode A (whole frame → 1640×590) | CLRerNet mode B (road band → 1640×320) | UFLDv2 (Fork H, masked) |
|---|---|---|---|
| lanes found on ≥1 frame | 11/12 | 10/12 | 10/12 |
| ego pair found | 10/12 | 10/12 | 11/12 |
| **ego pair on paint, both ≥ 0.5** | 0/12 | **3/12** | **3/12** |
| ego pair on paint, both ≥ 0.3 | 3/12 | 5/12 | 4/12 |
| ego-pair line support6 median | 0.31 | 0.65 | 0.65 |
| crossed-line type (gold all dashed) | — | dashed 5 · false solid 0 · unknown 7 | — |
| manual dash near-edge ≤ 15 px | — | **5/7** (006: 1.9/1.3/0.0 px on three consecutive dashes; 053 12.3; 035 14.7; misses 053-far 58, 011 108) | 3/7 (Fork J combo) |
- **Does CLRerNet's ego pair sit on paint more often tha…
07:27 · repliedCLRerNet ran on the pod and the pod is terminated, about 35 minutes and $0.45.
| | CLRerNet, full frame squashed | CLRerNet, road-band crop | UFLDv2 |
|---|---|---|---|
| Ego pair found | 10/12 | 10/12 | 11/12 |
| Ego pair on paint, both lines ≥ 0.5 | 0/12 | 3/12 | 3/12 |
| Ego pair on paint, ≥ 0.3 | 3/12 | 5/12 | 4/12 |
| Per-line paint support, median | 0.31 | 0.65 | 0.65 |
| Crossed-line type, gold all dashed | | 5 dashed, 0 false solid, 7 unknown | |
| Hand-picked dash rows matched within 15 px | | 5/7 | 3/7 with the combo |
**Swapping the model doesn't move the number.** The CULane state of the art lands on the same 3 of 12 as UFLDv2 with the same per-line support. The misses are the same clips: wet shaded road on 017, the parked-car curb scene on 016, and 008.
**Input geometry matters more than the model.** Squashing the full 1080p frame into CULane's aspect ratio gives 0 of 12 with lines running through the hood. The road-band crop is what makes either detector work. That's a cheap, general lesson for the CV step.
**The dash anchor became credible on the clips where the band crop validates.** On 006, three consecutive dashes land within 2 px of your hand-picked rows. It is still wrong on 011, where it rides a different marking.
**Where lanes stand after today.** Hood mask, paint validation, and a stronger detector all failed to lift ego-pair-on-paint past 3 of 12. What's left: train a marking-specific segmentation with dashed, solid and hatched classes; build lines directly from the paint skeleton without a detector hypothesis; or accept a human-confirmed "candidate line plus paint overlay" for the demo. And the gold still has zero solid-line clips, so line type can't be evaluated properly until the set widens.
All of it is in `docs/fault-lab-v1-3.md` secti…
07:39 · askedwhat is vllm pod? are you talking about runpod?
07:40 · repliedYes, RunPod. There were two pods on your account today:
- **`vlm2-cosmos3nano-4`** (id `ao420khj7w3x58`), an L40S running vLLM serving Cosmos3-Nano, the VLM used for the M1 experiments on Monday. It was created a few minutes before my first session started and was still receiving requests from another session after mine finished, so I never terminated it. That's the one I keep calling the "shared vLLM pod." At $1.09/h it is worth checking whether it's still running.
- **`fault-lab-clrernet`** (id `9yx58lwpqm9use`), the RTX 4090 I created today for the CLRerNet run. Terminated after about 35 minutes, confirmed gone.
I'll check the first one now so you have the current state rather than my assumption from two days ago.
07:40 · repliedNo pods are running on your account. The Cosmos vLLM pod was terminated at some point after Monday, presumably by whoever was using it, and the CLRerNet pod is gone. Nothing is billing.
07:41 · askedexplain to me what those mean. ego pair, ego pair on paint, both lines, ego pair on paint. what are they in easy words?
07:41 · repliedPlain version.
**Lane line.** A painted stripe on the road, like the dashed white line between two lanes.
**Ego pair.** "Ego" is the camera car, our car. The ego pair is the two lane lines on either side of it, the left edge and the right edge of the lane we're driving in. "Ego pair found" means the detector produced a line on the left and a line on the right of the car. It says nothing about whether those lines are in the right place.
**On paint.** Whether a detected line actually lies on real painted stripe. We check this against an independent source: a segmentation model that marks every pixel it believes is lane paint. For each detected line we ask what fraction of its points fall within 6 pixels of that paint. That fraction is the line's paint support. A line drawn along a curb, a shadow or the edge of the dashboard gets low support because there's no paint under it.
**Both lines ≥ 0.5.** The strict test: the left line and the right line each have at least half their points on paint. **≥ 0.3** is the loose test: at least 30% each.
**Ego pair on paint, 3 of 12.** Out of 12 clips, in only 3 did the detector put both edges of our lane on actual paint. In the other 9 it produced a pair, but at least one of the two lines runs somewhere that isn't a stripe, so any measurement built on it, line type, lane width, dash spacing, would be reading the wrong thing.
Why it matters for us: the modifier facts we want from lanes, whether the crossed line was solid or dashed, and the dash spacing that could give us meters, only mean anything if the line we're sampling along is the real stripe. That's why the on-paint number, not "lanes found," is the one I've been reporting.
07:46 · askedwho validated the segmentation model that is used to grade these? how do these look visually? i never got to see it. build an artifact to see the results on playable video with overlays. segmentation, line, different models toggled on videos that already ran.
07:46 · repliedStraight answer on the grader first: **nobody validated it on our footage.** The "on paint" reference is a public Mask2Former trained on Mapillary Vistas, and its "lane marking" class was taken as the definition of paint without checking it against a single hand-labelled frame from these clips. Two forks eyeballed overlays and one noted the mask is fat and bridges the gaps between dashes. So the 3 of 12 could be wrong in either direction, and you're right to want to see it before trusting any number built on it. Building the viewer now, with every layer toggleable on playable video.
07:47 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork L. Build a single self-contained HTML page that lets Ian watch the 12 probe clips with the lane/segmentation results overlaid and toggled per layer, so he can judge the models visually. Work only in /Users/liwimoi/projects/fault-lab (do not modify data/; do not touch ~/projects/cv). Mac, .venv-cv for the models (Fork H's src/faultlab/lanes_trial.py and egomask.py show how Vistas Mask2Former, UFLDv2 and YOLOPv2 were run; reuse those functions). Output HTML at /Users/liwimoi/projects/fault-lab/docs/lane-viewer.html (the parent will publish it as an Artifact; the page is wrapped in a doctype/head/body skeleton at publish time, so write only <title>, <style>, and body content — no <html>/<head>/<body> tags; put <title> first).
Hard constraints of the artifact host: NO external resources except scripts from cdnjs (…
07:47 · repliedTask: build a self-contained HTML lane-overlay video viewer for the 12 probe clips at `docs/lane-viewer.html`.
07:47 · repliedThe viewer is being built: each clip's 3-second window around contact re-encoded small enough to embed, the segmentation, ego mask and skeleton rendered per frame, UFLDv2 and YOLOPv2 lines on every frame, CLRerNet on the frames it ran, and your hand-picked dash rows as ticks. Every layer toggles independently, and each line carries its paint-support score so you can see where the grader agrees or disagrees with your eyes. I'll publish it as soon as it's ready.
07:54 · repliedBuilt the self-contained lane overlay viewer at `docs/lane-viewer.html` (6.26 MB); data builder in `src/faultlab/lane_viewer_build.py` (template embedded; `--assemble` rebuilds the HTML from `runs/lanes/viewer/<clip>.json`). Nothing under `data/` or `~/projects/cv` touched.
**What it contains**: 12 clips × 45 frames (t_c−2 → t_c+1, 15 fps, 640×360 H.264 baseline, 126–356 KB each), per frame UFLDv2 and YOLOPv2 polylines (≤40 pts, paint support vs the skeletonised Vistas mask at 1080p, ego-pair flag), Vistas paint + skeleton masks every 3rd frame (15/clip, ~380 B palette PNGs), one ego mask per clip, CLRerNet A/B lines on the frames it ran (sparse badge), manual dash ticks where the frame falls inside the window.
**Validation**: only external ref is the fonts.googleapis.com stylesheet; no html/head/body/doctype tags; embedded JSON parses; every clip has a video data URI and 45 frames; `node --check` on the script passes.
**Per clip (frames with output of 45)**
| clip | leaf | video KB | UFLDv2 | YOLOPv2 | CLRerNet frames | manual dash |
|---|---|---|---|---|---|---|
| 003 | 차42-2 | 298 | 24 | 45 | 3 | — |
| 006 | 차43-4 | 253 | 36 | 45 | 3 | 1 |
| 008 | 차43-4 | 305 | 26 | 45 | 1 | — |
| 011 | 차43-3 | 348 | 31 | 45 | 3 | — (manual frame t=0.5 s is outside the window) |
| 016 | 차42-2 | 126 | 40 | 45 | 3 | — |
| 017 | 차43-4 | 227 | 4 | 45 | 0 (CLRerNet found nothing) | — |
| 023 | 차43-2 | 256 | 45 | 45 | 3 | — |
| 027 | 차47-1 | 292 | 37 | 45 | 3 | — |
| 035 | 차43-2 | 327 | 38 | 39 | 3 | — (manual frame t=2.5 s outside window) |
| 042 | 차43-2 | 248 | 21 | 45 | 1 | — |
| 053 | 차43-4 | 348 | 45 | 45 | 3 | 1 |
| 058 | 차43-4 | 256 | 45 | 45 | 3 | — |
No model errored on any clip. Caveats: 006's window is 7–10 s because t_c = 10; video frames were cut with accurate `-ss` seeki…
07:54 · asked<task-notification>
<task-id>aefe033df3413acad</task-id>
<tool-use-id>toolu_01KRgve8CK3wYiFF4TNbmUa2</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/aefe033df3413acad.output</output-file>
<status>completed</status>
<summary>Agent "Build lane overlay video viewer artifact" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Built the self-contained lane overlay viewer at `docs/lane-viewer.html` (6.26 MB); data builder in `src/faultlab/lane_viewer_build.py` (template embedded; `--assemble` rebuilds the HTML from `runs/lanes/viewer/<clip>.json`). Nothing under `data/` or `~/projects/cv` touched.
**What it contains**: 12 clips × 45 frames (t_c−2 → t_c+1, 15 fps, 640×360 H.264 baseline, 126–356 KB each), per frame UFLDv2 and YOLOPv2 polylines (≤40 pts, paint support vs the skeletonised Vistas mask at 1080p, ego-pair flag), Vistas paint + skeleton masks every 3rd frame (15/clip, ~380 B palette PNGs), one ego mask per clip, CLRerNet A/B lines on the frames it ran (sparse badge), manual dash ticks where the frame falls inside the window.
**Validation**: only external ref is the fonts.googleapis.com stylesheet; no html/head/body/doctype tags; embedded JSON parses; every clip has a video data URI and 45 frames; `node --check` on the script passes.
**Per clip (frames with output of 45)**
| clip | leaf | video KB | UFLDv2 | YOLOPv2 | CLRerNet frames | manual dash |
|---|---|---|---|---|---|---|
| 003 | 차42-2 | 298 | 24 | 45 | 3 | — |
| 006 | 차43-4 | 253 | 36 | 45 | 3 | 1 |
| 008 | 차43-4 | 305 | 26 | 45 | 1 | — |…
07:54 · repliedViewer is live: https://claude.ai/code/artifact/bf577a67-024b-408b-ba1a-f4aa72c69d3e (also saved as `docs/lane-viewer.html`, 6.3 MB, fully self-contained).
**What you're looking at.** Each clip is the 3 seconds around contact, 2 s before to 1 s after, with a vertical line at t_c. Layers toggle independently:
- **Vistas paint mask** and its **skeleton**: this is the grader. If the white fill isn't on real stripes, every "on paint" number is suspect. This is the layer to judge first.
- **Ego mask**: the hood and mount region the segmenter removes.
- **UFLDv2** and **YOLOPv2** lines on every frame, each labelled with its paint support, green at or above 0.5, amber 0.3 to 0.5, red below.
- **CLRerNet** lines only on the frames it ran, with a badge, since it ran on the pod for three to five frames per clip.
- **Manual dash ticks** where your hand-picked rows fall inside the window, on 006 and 053.
Use comma and period to step frames, and the "ego pair only" filter to hide the other lanes.
**Two caveats before you draw conclusions.** The overlay-to-video alignment could be off by one frame; it was cut with accurate seeking but not verified by eye. And 017 shows almost nothing from any model, which is the wet shaded road, not a rendering bug.
If the Vistas fill looks wrong to you on several clips, say which ones. That would mean the 3 of 12 is measuring the grader, not the detectors, and the fix is a hand-labelled paint mask on a few frames to calibrate it.
08:00 · askedufld2 lines look zigzags flickers a lot, and i only see clrnet lines rarely (only shown for certain frames) but they are pretty good. can you reason them?
08:01 · askedufld2 lines look zigzags flickers a lot, and i only see clrnet lines rarely (only shown for certain frames) but they are pretty good. can you reason them? and what's the A B difference in CLRNet?
08:01 · repliedLet me check one thing before answering, because it may explain the zigzag: how the UFLDv2 frames were fed, full frame or road band.
08:02 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork M. Make the lane comparison fair and refresh the viewer. Work only inside /Users/liwimoi/projects/fault-lab; do not modify data/; do not touch ~/projects/cv.
1. UFLDv2 with the band crop (local, .venv-cv, KMP_DUPLICATE_LIB_OK=TRUE): reuse src/faultlab/lanes_trial.py's UFLDv2 wrapper but feed it CLRerNet's mode-B geometry — rows [y_h−60, hood) resized to 1600×320 directly (skip the crop_ratio resize path; the model's input is the 320-row band), map predictions back to 1920×1080. y_h and hood per clip as used in runs/lanes/clrernet/params.json / runs/lanes/<clip>/ego.json. Run on all 45 frames of each clip's viewer window (t_c−2.0 → t_c+1.0 at 15 fps; frame list and t0 in runs/lanes/viewer/<clip>.json from Fork L's build). Also keep the old full-frame UFLDv2 output as a separate layer ("UFLDv2 full") so both can…
08:02 · repliedRunning now: UFLDv2 re-fed with the same road-band crop CLRerNet got, CLRerNet mode B on every frame of all 12 windows on a fresh pod, a flicker metric for each so the zigzag is a number rather than an impression, and the viewer rebuilt with both new layers on the same URL. The pod gets terminated by the fork when the run ends.
08:02 · repliedTask: run UFLDv2 with the band crop and CLRerNet B on all 45 window frames per clip, score both, rebuild the viewer, terminate the pod.
08:18 · askedwhat's a road-band crop? and what are pain skeleton vs vistas paintmask. and why do they look like remnants from the previous frame? (just an overlay bug)
08:18 · repliedThree answers, and the third one is a real defect I'll fix in the rebuild that's already running.
**Road-band crop.** The lane models were trained on images shaped 1640×590 with the top 270 rows discarded, so what they actually learned on is a wide, short strip in which the road fills the height. A dashcam frame is 1920×1080 with sky at the top and the hood at the bottom. If you squash the whole frame into that shape, the road ends up small and in the wrong place and the hood occupies the region where the model expects near-field lane paint. The band crop instead cuts one horizontal strip, from 60 px above the horizon down to the hood line, and resizes only that strip to the model's input size. The road then looks the way it did in training. That single change took CLRerNet from 0 of 12 to 3 of 12 on paint, which is why I'm now giving UFLDv2 the same treatment.
**Vistas paint mask versus paint skeleton.** The paint mask is every pixel the Mapillary Vistas segmentation model labels as lane marking, drawn as a filled region. It is somewhat fat, because the model runs at reduced resolution and the mask is upscaled. The skeleton is that same mask thinned down to a one-pixel centerline. The scoring uses the skeleton: a detected lane point counts as "on paint" if it lies within 6 px of the skeleton, and a line's support is the fraction of its points that do. The skeleton exists so that a fat blob doesn't give credit to a line running along its edge.
**The remnants.** Not a canvas bug, a sampling artifact, and it's misleading enough to count as a bug. The segmentation was computed on only every third frame, 15 of the 45, and the viewer shows the nearest computed mask for the frames in between. So the mask stays put for two frames while the video moves on, and it reads as a …
08:18 · repliedQueued into the running rebuild: dense masks on every frame, and per-frame support labels against each frame's own skeleton. The republished viewer will have no lag between video and overlay. I'll post the link and the fair comparison table when it lands.
08:27 · repliedTask: band-crop UFLDv2 + CLRerNet B on all 45 window frames × 12 clips, scored with the paint metric, viewer rebuilt with per-frame masks; pod terminated.
**Files**: `src/faultlab/lanes_window.py` (window frames → band crops + UFLDv2 band), `src/faultlab/lanes_window_score.py` (scoring + writes layers/masks into the viewer JSON), `runs/lanes/ufld_band/<clip>.json`, `runs/lanes/clrernet/raw/window_B.json` (540 frames, 0.017 s/frame on the 4090), `runs/lanes/clrernet/{window_params.json, pod_window_infer.py}`, `runs/lanes/window_masks/` (540 per-frame Vistas mark/skeleton/dist npz), `runs/lanes/clrernet/scores.md` §8, `runs/lanes/clrernet/window_scores.json`, `docs/lane-viewer.html` (same path; parent republishes). `lane_viewer_build.py` template gained the two layers. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Scores** (support = fraction of a line's points within 6 px of the frame's own Vistas skeleton; ego pair = nearest line each side of centre at the hood row)
| model | 3 exact frames: pair found / on paint ≥0.5 / ≥0.3 (clips) | 3-frame support median | all 45 frames: pair found / ≥0.5 / ≥0.3 (frames of 540) | 45-frame support median | stability, median frame-to-frame \|Δx\| hood row / mid-band (px) |
|---|---|---|---|---|---|
| UFLDv2 full | 11/12 · 0/12 · 2/12 | 0.13 | 311 · 0 · 38 | 0.15 | 4.7 / 3.5 |
| UFLDv2 band | 7/12 · 2/12 · 3/12 | 0.22 | 235 · 11 · 29 | 0.20 | 2.3 / 4.3 |
| CLRerNet B | 9/12 · 3/12 · 4/12 | 0.46 | 249 · 58 · 84 | 0.56 | 4.7 / 3.3 |
Clips where CLRerNet B's pair is on paint on many frames: 053 (39/45), 042 (10), 035 (6). UFLDv2 band: 053 (6), 035 and 042 (2 each). UFLDv2 full: never.
**Reading**
- The band crop helps UFLDv2 (support 0.13 → 0.22, hood-row jitter 4.7 → 2.3 px) but it does not reach CLRerNet: sa…
08:27 · asked<task-notification>
<task-id>a3f8051ddcc475bff</task-id>
<tool-use-id>toolu_01Cti6Rq5MFf9u7tRVbqbiRZ</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a3f8051ddcc475bff.output</output-file>
<status>completed</status>
<summary>Agent "Band-crop UFLDv2 + full-window CLRerNet, rebuild viewer" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: band-crop UFLDv2 + CLRerNet B on all 45 window frames × 12 clips, scored with the paint metric, viewer rebuilt with per-frame masks; pod terminated.
**Files**: `src/faultlab/lanes_window.py` (window frames → band crops + UFLDv2 band), `src/faultlab/lanes_window_score.py` (scoring + writes layers/masks into the viewer JSON), `runs/lanes/ufld_band/<clip>.json`, `runs/lanes/clrernet/raw/window_B.json` (540 frames, 0.017 s/frame on the 4090), `runs/lanes/clrernet/{window_params.json, pod_window_infer.py}`, `runs/lanes/window_masks/` (540 per-frame Vistas mark/skeleton/dist npz), `runs/lanes/clrernet/scores.md` §8, `runs/lanes/clrernet/window_scores.json`, `docs/lane-viewer.html` (same path; parent republishes). `lane_viewer_build.py` template gained the two layers. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Scores** (support = fraction of a line's points within 6 px of the frame's own Vistas skeleton; ego pair = nearest line each side of centre at the hood row)
| model | 3 exact frames: pair found / on paint ≥0.5 / ≥0.3 (clips) | 3-frame support median | all 45 frames: pair found / ≥0.5 / ≥0.3 (frames of 540) | 45-frame support median | s…
08:28 · repliedViewer is republished at the same link: https://claude.ai/code/artifact/bf577a67-024b-408b-ba1a-f4aa72c69d3e. Masks are now on every frame so the ghosting is gone, and two new layers are in: "UFLDv2 band" in green and "CLRerNet B" dense in yellow on all 45 frames.
**The fair comparison**, all three scored identically on the same frames:
| Model | On paint ≥ 0.5, frames of 540 | Line support, median | Hood-row jitter, px | Mid-band jitter, px |
|---|---|---|---|---|
| UFLDv2, full frame | 0 | 0.13 | 4.7 | 3.5 |
| UFLDv2, band crop | 11 | 0.22 | 2.3 | 4.3 |
| CLRerNet B | 58 | 0.46 | 4.7 | 3.3 |
**What this settles about what you saw.**
- The band crop is what tames UFLDv2's zigzag: hood-row jitter halves and support nearly doubles. So part of the ugliness was our input, not the model.
- But it doesn't catch CLRerNet. On identical input, CLRerNet's lines sit on paint five times as often. Placement is architecture, jitter is geometry.
- CLRerNet's smoothness is within a frame, one curve per lane. Frame to frame it moves about as much as the others, which you'll now see with the dense layer.
- Nothing works on 017, the wet shaded road, and the earlier 3 of 12 "pair on paint" figure isn't directly comparable to this table because that one scored a straight-line fit rather than the raw polyline.
The one open question from before still stands: whether the Vistas paint mask itself is trustworthy. Now that it's frame-accurate in the viewer, the clips to judge it on are 053, where CLRerNet is on paint 39 of 45 frames, and 003 or 016, where nothing scores. If the white fill looks wrong on those, the grader needs a few hand-labelled frames.
08:42 · askedUFLDv2 is often not a lane, what i meant by zigzag is the line itself is weird. not a lane. CLRerNet shows great lines, but is not always there. wonder if that's the tradeoff
08:43 · repliedYes, and it is structural rather than accidental.
**Why UFLDv2 draws non-lanes.** It doesn't propose lanes. For each of four lane slots and each of 72 rows it picks a column, and the only "no lane here" decision is per row. There is no step where the whole curve has to make sense, so when evidence is weak it still emits a slot's worth of points, stitched from wherever each row's classifier landed: a curb for a few rows, a shadow for a few more. What you saw as a weird line is that stitching. It is a recall-first design: something is almost always output, and on hard frames that something is garbage.
**Why CLRerNet is good but absent.** It proposes whole-lane anchors, scores each as one curve, drops any below a confidence threshold, and suppresses duplicates. A lane exists only if a coherent curve hypothesis clears the bar. That is precision-first: on frames where nothing coherent exists it outputs nothing. In our numbers it found an ego pair on 249 of 540 frames against UFLDv2's 311, but was on paint 58 times against 11.
**So it's a tradeoff, but not one you have to accept as-is.** Two levers:
- **Threshold.** CLRerNet's confidence cutoff for CULane is around 0.4. Lowering it to 0.3 or 0.2 trades some on-paint precision for more frames with a lane. Where that curve bends is measurable on the same 540 frames.
- **Temporal tracking.** A lane doesn't vanish for 0.2 s and reappear. Carrying the last confident curve forward for a few frames, or fitting a light tracker over lane parameters, fills the gaps without admitting garbage. This is how ADAS lane keeping has always handled it, and it is the right pairing with a precision-first detector.
What tracking won't fix is the clips where CLRerNet finds nothing at all, 017 on the wet shaded road, most of 008 and 027. Those …
23:43 · askedLet’s try how ADAS lane keeping handles it. I mean they have it on the whole time right? But while the vehicle is moving. How is this even possible. Because they have depth and trajectory data? Or is it because they have more than one camera?
23:44 · repliedNeither depth nor extra cameras. Production lane keeping, Mobileye-style, runs on one forward mono camera. It stays "on" through frames where a detector alone would find nothing because of four things, none of which is a better detector.
**1. The camera is calibrated and fixed.** Known focal, height and pitch turn image lines into metric lane geometry in the car's frame. That lets the system reason about lanes as physical objects, 3.0 to 3.75 m apart, parallel, gently curving, rather than as pixels. Anything that violates those constraints is rejected before it can become a zigzag. This is the garbage filter.
**2. The car tells it how it moved.** Wheel speed and yaw rate come off the CAN bus every few milliseconds. If the lane was 1.6 m to the left and the car moved 0.4 m forward and turned 0.2 degrees, the system knows where the lane must be now without seeing it. That's the trajectory data you're thinking of, but it is odometry from the car, not vision depth.
**3. A tracker carries the lane between detections.** The lane is a small state, lateral offset, heading, curvature, per line, run through a Kalman filter. Detections are measurements; between them the state predicts. A dropout of a few frames is just prediction. Wrong detections that don't match the prediction get gated out.
**4. It isn't actually on the whole time.** Rain, faded paint and construction make it drop to "lane not available" and warn the driver. Highway availability is high, city availability lower. HD-map priors are the fifth ingredient in newer systems, not stereo.
What we have and lack: the camera is fixed within a clip, and the horizon is recoverable per clip, so the constraints can be expressed in the inverse-row domain even without meters. We have no CAN odometry, but over a 3 s window c…
23:44 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork N. Build an ADAS-style lane tracker over CLRerNet B detections and measure whether it fills the gaps without admitting garbage. Work only in /Users/liwimoi/projects/fault-lab (.venv-cv, KMP_DUPLICATE_LIB_OK=TRUE); do not modify data/; no pod needed.
Inputs: runs/lanes/clrernet/raw/window_B.json (540 frames = 12 clips × 45, lanes in 1920×1080 coords with scores), per-clip y_h (horizon) and hood row from runs/lanes/clrernet/window_params.json / runs/lanes/<clip>/ego.json, per-frame Vistas skeleton/distance maps in runs/lanes/window_masks/, the scoring code in src/faultlab/lanes_window_score.py (paint support, ego-pair selection) and the viewer JSON in runs/lanes/viewer/<clip>.json.
Design (write it in the module docstring of src/faultlab/lane_track.py):
- Work in the inverse-row domain u = 1/(y − y_h) (y below …
23:44 · repliedThe tracker is being built along those lines: a Kalman filter over the ego pair in the inverse-row domain, left and right coupled through shared curvature and a slowly varying lane width, CLRerNet detections as gated measurements, prediction-only for up to 0.27 or 0.53 s before dropping the track, a hold-last baseline for comparison, and a confidence-threshold sweep. It gets scored on the same 540 frames with the same paint metric, and added to the viewer as a tracked layer with solid strokes for measured frames and dashed for predicted ones. I'll report when it lands.
23:48 · askedAnd these models are supposed to output lanes? Like real world lanes? Or just ego lane and relative lanes? Do they work on curves? What about cuves at an intersection?
23:49 · repliedWhat they output is narrower than "lanes."
**What comes out.** CULane-family models, UFLDv2 and CLRerNet alike, emit up to four lane *lines* as image-space curves: the two bounding the ego lane and one more on each side. No lane objects, no lane count, no IDs beyond left-to-right order, no real-world coordinates. Converting to metric position needs the calibration we've been chasing. So "which lane am I in" on a five-lane road is not answerable from these models alone, and "is the other car two lanes over" needs the tracks plus the lines plus geometry.
**Curves.** Moderate ones, yes. The training data includes curved roads and the anchors are refined to bend. Sharp near-field curvature, like the arc of a car actually turning, is beyond what the anchors represent, and the models drift or drop out there.
**Intersections.** This is where they fail by design, and it matters for us because 차43-4 and 차47-1 cases live at intersections and stop lines. CULane has an explicit "no line" category for them. Lane lines end at the stop line, the crossing is unpainted or carries guide arcs the models never learned, and the ego lane geometry the anchors assume, lines running bottom to top, doesn't hold. At an intersection the useful marks are stop lines, crosswalks, turn arrows and guide lines, which is road-marking segmentation, not lane detection. That is the Mapillary v2-class or Grounded-SAM route from earlier.
**What would give real-world lanes.** 3D lane models, OpenLane, PersFormer, LATR, output lanes in the car's coordinate frame, but they assume a calibrated camera with known height and pitch. new-knia evaluated and rejected them for that reason. Multi-camera BEV mapping models, MapTR-style, produce actual lane topology but need surround cameras. Neither applies to a single…
23:51 · repliedTask: built an ADAS-style ego-pair lane tracker over CLRerNet B window detections, scored gap-filling against paint, added the viewer layer. Done.
**Files**: `src/faultlab/lane_track.py` (design in docstring), `runs/lanes/track/scores.md`, `results.json`, `src/faultlab/lane_viewer_build.py` (new layer "CLRerNet 추적"), `runs/lanes/viewer/<clip>.json` (`clrt` per frame), `docs/lane-viewer.html` re-assembled (7.67 MB; only external ref is the fonts stylesheet, no html/head/body tags, JSON parses, 45 `clrt` frames on all 12 clips). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Scores (540 frames)**
| method | frames with pair | pair on paint ≥0.5 | ≥0.3 | support median | predicted frames | predicted on paint ≥0.5 | garbage admitted (measured lines <0.3) | hood-row jitter px |
|---|---|---|---|---|---|---|---|---|
| CLRerNet B raw | 249 | 58 | 82 | 0.55 | 0 | — | 218/639 | 4.7 |
| hold-last K=4 | 330 | 58 | 83 | 0.44 | 81 | 0/81 | 218/639 | 3.3 |
| hold-last K=8 | 366 | 58 | 83 | 0.38 | 117 | 0/117 | 218/639 | 2.5 |
| Kalman K=4 | 253 | 55 | 89 | 0.55 | 62 | 2/62 | 142/478 | 5.2 |
| Kalman K=8 | 291 | 55 | 88 | 0.42 | 100 | 2/100 | 142/478 | 5.8 |
**Plain answer: tracking raises frames-with-pair but does not keep on-paint precision.** Predictions are almost never on paint, and not only because paint is hidden: of 146 Kalman-predicted side-lines, 71 had real paint within 40 px on most rows and only 6 of those scored ≥0.5 (median 0.14); hold-last 3 of 73. The ego moves laterally during gaps, so constant-velocity or frozen lines leave the stripe within 0.3–0.5 s. The one real gain is the association gate: garbage measured lines drop from 34% to 30%, and on 016 it correctly discards the off-paint pair (42 → 5 frames).
**What happened along the way**…
23:51 · asked<task-notification>
<task-id>ad4a3198e8b73abb5</task-id>
<tool-use-id>toolu_01VHM2tFnKQyqeURz2zgBoFD</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/ad4a3198e8b73abb5.output</output-file>
<status>completed</status>
<summary>Agent "Lane tracker over CLRerNet detections" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: built an ADAS-style ego-pair lane tracker over CLRerNet B window detections, scored gap-filling against paint, added the viewer layer. Done.
**Files**: `src/faultlab/lane_track.py` (design in docstring), `runs/lanes/track/scores.md`, `results.json`, `src/faultlab/lane_viewer_build.py` (new layer "CLRerNet 추적"), `runs/lanes/viewer/<clip>.json` (`clrt` per frame), `docs/lane-viewer.html` re-assembled (7.67 MB; only external ref is the fonts stylesheet, no html/head/body tags, JSON parses, 45 `clrt` frames on all 12 clips). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Scores (540 frames)**
| method | frames with pair | pair on paint ≥0.5 | ≥0.3 | support median | predicted frames | predicted on paint ≥0.5 | garbage admitted (measured lines <0.3) | hood-row jitter px |
|---|---|---|---|---|---|---|---|---|
| CLRerNet B raw | 249 | 58 | 82 | 0.55 | 0 | — | 218/639 | 4.7 |
| hold-last K=4 | 330 | 58 | 83 | 0.44 | 81 | 0/81 | 218/639 | 3.3 |
| hold-last K=8 | 366 | 58 | 83 | 0.38 | 117 | 0/117 | 218/639 | 2.5 |
| Kalman K=4 | 253 | 55 | 89 | 0.55 | 62 | 2/62 | 142/478 | 5.2 |
| Kalman K=8 | 291 | 55 | 88 | 0.42 | 100 | 2/100 | 142/478 | 5.8 |
**Plain answer…
23:51 · repliedThe tracker is in the viewer as "CLRerNet 추적", solid strokes where it measured and dashed where it predicted, same link. The result answers your ADAS question the hard way.
| Method | Frames with ego pair | On paint ≥ 0.5 | Predicted frames | Predicted frames on paint |
|---|---|---|---|---|
| CLRerNet B raw | 249 | 58 | 0 | |
| Hold last, 0.5 s | 366 | 58 | 117 | 0 |
| Kalman, 0.27 s | 253 | 55 | 62 | 2 |
| Kalman, 0.53 s | 291 | 55 | 100 | 2 |
**Tracking fills frames, not paint.** Of the 146 predicted lines, 71 had real paint within 40 px, and only 6 of those landed on it. The reason is specific to our data: these are accident clips, and during the gaps the ego car is changing lanes. A line frozen or extrapolated at constant velocity leaves the stripe within a third of a second. That is exactly the piece ADAS has and we don't: the car's own lateral speed and yaw rate from the CAN bus, which tells the filter where the lane must have moved. Without odometry, prediction across a lane change is guessing.
**What it did buy.** The association gate rejects off-paint detections, garbage measured lines dropped from 34% to 30%, and on 016 it threw out the wrong pair on 37 of 42 frames.
**Two things I learned building it.** A quadratic in the inverse-row domain, the textbook lane model, misfits CLRerNet's curves by 24 to 32 px on the lane-change clips, so the tracker had to be non-parametric. And the pod run saved only lane points, not confidence scores, so the threshold sweep needs a rerun if you want it.
**Where this leaves it.** The missing ingredient is ego lateral motion. We can estimate it from vision: lane-intercept drift on measured frames, or the optical-flow module once the hood mask restricts it to road. Feeding that into the prediction step is the one change tha…
23:52 · askedSo the 3D lanes require more data? What are they? Can we compute them and input them from what we’ve learned?
23:53 · repliedNot more sensors, more *parameters*. Same single camera.
**What they are.** OpenLane-family models, PersFormer, Anchor3DLane, LATR, take one image plus the camera's intrinsics (focal, principal point) and its pose relative to the ground (height, pitch, roll), and output each lane line as a 3D polyline in the car's frame: lateral offset, forward distance, height above ground, plus a type label from 14 classes including dashed, solid, double and road edge. Inside, they use those parameters to warp image features onto a ground plane, learned IPM essentially, so the geometry comes out in meters. Trained on Waymo footage, 200k frames, US roads.
**What "requires more data" means.** They need the camera model as an input. On a fleet vehicle that's a factory calibration. On a random dashcam it's exactly what we spent two days trying to recover:
- Horizon and pitch: recoverable, ±18 px. Roll: under 4°. Usable.
- Focal: ±25% disagreement across sources. Errors here scale forward distances.
- Height: unknown. Typical dashcam 1.2 to 1.5 m. Our depth-based solve gave 0.99 m and was biased.
**Can we compute inputs from what we learned?** Partly, and one piece of geometry helps a lot: on a flat road, lateral position depends on height and horizon only, the focal cancels. Forward distance needs focal times height. So if the ego pair is on paint and lane width is taken as 3.0 to 3.5 m, height solves directly from the pixel spacing of the two lines at any row. That turns the "on paint 3 of 12" problem into the calibration bottleneck, which is at least a single bottleneck. With height and horizon solved and focal assumed from MoGe-2, we'd have an approximate camera model per clip.
**The trade-off.** Given approximate parameters, a 3D lane net gives us lanes in meters with types, on K…
23:57 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork O. Run a 3D lane detector (OpenLane weights) on the 12 probe clips using our estimated per-clip camera model, and judge it by lane-width plausibility and by projecting its lanes back into the image against paint. Local work only in /Users/liwimoi/projects/fault-lab (.venv-cv); do not modify data/; do not touch ~/projects/cv. Licence irrelevant.
Pod: `cehi764xx5ew5o` (RTX 4090, runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404, 22/tcp, your key). Get ip/port via mcp__plugin_runpod_runpod__get-pod (poll ~30 s until runtime.ports has the 22 mapping); proxy `ssh cehi764xx5ew5o-64411574@ssh.runpod.io` for shell meanwhile. Upload is slow (~25–400 KB/s): send only what's needed (36–60 frames as JPEG q90 in one tar). **Terminate the pod when finished or blocked: mcp__plugin_runpod_runpod__delete-pod podId cehi764xx5ew5o…
23:57 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork P. Build the baseline the 3D-lane test will be compared against: our own flat-road projection of CLRerNet B lines into the car's ground frame, per frame, and derive from it the ego lateral motion the lane tracker was missing. Local only, /Users/liwimoi/projects/fault-lab, .venv-cv (KMP_DUPLICATE_LIB_OK=TRUE); do not modify data/; no pod. Do not write to runs/lanes/3d/ (Fork O owns it); use runs/lanes/ground/.
Inputs: runs/lanes/clrernet/raw/window_B.json (540 frames, 1920×1080 lane points), per-clip horizon y_h and roll from runs/depth/intrinsics/fused.json and runs/depth/intrinsics/*.json (GeoCalib), focal f = MoGe-2 per-clip median (same files), hood row from runs/lanes/<clip>/ego.json, paint support per line from runs/lanes/clrernet/window_scores.json, Vistas skeletons in runs/lanes/window_masks/.
Geometry…
23:57 · repliedBoth are running. A new pod (id `cehi764xx5ew5o`, $0.74/h) is up for the 3D lane model, and the fork terminates it when done. In parallel, the local baseline: CLRerNet lines projected to the ground plane with the per-clip horizon and a camera height solved from lane width, then the ego lateral motion from that, fed back into the tracker's prediction step to see whether predicted lines finally land on paint. I'll report both together.
00:03 · repliedTask: flat-road ground projection of CLRerNet B lines, per-clip H, lane widths, ego lateral motion, and ego-motion-aided tracking; report in `runs/lanes/ground/`.
**Files**: `src/faultlab/lane_ground.py` (design in docstring), `src/faultlab/lane_track.py` (+`--ego-motion DIR`, `--out DIR`; default behaviour unchanged), `runs/lanes/ground/{scores.md, results.json, <clip>.json, vis/<clip>.png ×12, track/scores.md, track/results.json}`. Nothing under `data/` or `runs/lanes/3d/` touched; nothing committed. Viewer lane-width readout skipped (reassembly cost, no gain).
**1. H from lane width** (ego pair on paint ≥0.5 both sides): only 5/12 clips have any usable frame.
| clip | H (m) | IQR | frames |
|---|---|---|---|
| 053 | 1.30 | 0.17 | 39 |
| 042 | 1.62 | 0.05 | 10 |
| 035 | 1.16 | 0.06 | 6 |
| 003 | 0.79 | 0.06 | 2 |
| 008 | 1.03 | 0.06 | 1 |
| others | 1.3 default | — | 0 |
**2. Lane width at 10 m** (f cancels for lateral): 053 3.44 (39/45 within ±0.5 of 3.25), 035 3.32 (16/19), 042 3.41 (9/12), 003 3.65 (16/21), 008 3.33 (4/4). Clips whose pair is off paint read 4.1–5.4 m (006, 016, 023, 027) — width is a usable ego-pair validity check.
**3. Ego lateral motion** (lines linked at Z=6 m; road-only Farneback flow with vehicle boxes masked as fallback): sign correct on both lane-change clips, magnitude short — 008 −2.2 m, 011 −1.1 m at t_c (expected ≈ −3.25). Lines-v vs flow-v correlation 011 0.86, 035 0.79, 042 0.59, 008 0.58; straight clips drift 1–2 m over 3 s (027, 035, 042; 017 +2.9 m flow-only), i.e. the flow is not an odometry substitute.
**4. Tracker with ego shift** (prediction-only sides moved by ΔX_line·(y−y_h)/H):
| method | pair frames | pair ≥0.5 | predicted | predicted ≥0.5 | garbage (measured <0.3) | hood jitter px |
|---|---|---|---|---|---|---|
| CL…
00:03 · asked<task-notification>
<task-id>ae1ffb7c041773022</task-id>
<tool-use-id>toolu_01McAmfDpessYNCQtgRtgtcU</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/ae1ffb7c041773022.output</output-file>
<status>completed</status>
<summary>Agent "Flat-road projection of CLRerNet lines" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: flat-road ground projection of CLRerNet B lines, per-clip H, lane widths, ego lateral motion, and ego-motion-aided tracking; report in `runs/lanes/ground/`.
**Files**: `src/faultlab/lane_ground.py` (design in docstring), `src/faultlab/lane_track.py` (+`--ego-motion DIR`, `--out DIR`; default behaviour unchanged), `runs/lanes/ground/{scores.md, results.json, <clip>.json, vis/<clip>.png ×12, track/scores.md, track/results.json}`. Nothing under `data/` or `runs/lanes/3d/` touched; nothing committed. Viewer lane-width readout skipped (reassembly cost, no gain).
**1. H from lane width** (ego pair on paint ≥0.5 both sides): only 5/12 clips have any usable frame.
| clip | H (m) | IQR | frames |
|---|---|---|---|
| 053 | 1.30 | 0.17 | 39 |
| 042 | 1.62 | 0.05 | 10 |
| 035 | 1.16 | 0.06 | 6 |
| 003 | 0.79 | 0.06 | 2 |
| 008 | 1.03 | 0.06 | 1 |
| others | 1.3 default | — | 0 |
**2. Lane width at 10 m** (f cancels for lateral): 053 3.44 (39/45 within ±0.5 of 3.25), 035 3.32 (16/19), 042 3.41 (9/12), 003 3.65 (16/21), 008 3.33 (4/4). Clips whose pair is off paint read 4.1–5.4 m (006, 016, 023, 027) — width is a usable ego-pair validity check.
**3. Ego lateral motion** (lines linked …
00:04 · repliedThe ground-projection baseline is in. Short version: the flat-road model gives real lateral geometry where the ego pair is on paint, camera height 1.16 to 1.62 m on five clips and lane width 3.3 to 3.6 m at 10 m, and lane width turns out to be a good validity test since off-paint pairs read 4 to 5 m wide. But feeding the derived ego motion into the tracker did not help: predicted lines still land on paint 1 in 100 times. The ego signal has the right sign but is too small on the lane-change clips and drifts on straight ones, so vision-only odometry over 3 seconds is not a CAN-bus substitute. Waiting on the 3D lane model before the combined write-up.
00:14 · repliedTask: PersFormer (OpenLane v1.2) run on the 12 probe clips with our per-clip camera model, scored on lane-width plausibility and back-projected paint support; pod terminated.
**Files**: `runs/lanes/3d/{scores.md, results.json, camera_params.json, versions.txt, pod_infer.py, raw/persformer_out.json, vis/<clip>.png ×12}`, `src/faultlab/lanes_3d_score.py`. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Model that ran**: PersFormer (EfficientNet-B7, OpenLane v1.2 ckpt, 652 MB via gdown) on torch 2.8/cu128 after patching its NMS + ms_deform_attn CUDA ops and numpy-2 aliases; `load_state_dict` 0 missing / 0 unexpected. LATR (mmcv 1.5 + mmdet3d) and Anchor3DLane (mmcv-full + mmseg 0.26) were not attempted. **One approximation that matters**: PersFormer derives its anchor-offset stds and 2D anchors from the label set; with no OpenLane labels on the pod I fed it a synthetic OpenLane-format set (straight/curved lanes, cam height 2.1 m), so lateral offsets are scaled by approximate stds (0.7–2.8 m per y-step). Camera: 1920×1080 letterboxed to 1920×1280, K=[[f,0,960],[0,f,640]], pitch from horizon (far-ground check lands exactly on y_h), roll ignored, H solved from CLRerNet pair width on 8 clips (0.83–1.60 m) else 1.3 m.
**Results (36 frames, 12 clips; PersFormer vs CLRerNet B on the same exact frames and masks)**
| | 3D lanes ≥1 / 0 (frames) | ego pair on paint ≥0.5 / ≥0.3 (frames) | ≥0.5 (clips) | line support6 median | width@10 m clips in 2.8–3.8 | width@10 m median · IQR |
|---|---|---|---|---|---|---|
| PersFormer, f=MoGe, H solved | 25/36 · 11/36 | 1/6 · 2/6 | **1/12** | 0.25 | **3/8** | 4.53 · 1.50 |
| PersFormer, f=GeoCalib | 26/36 · 10/36 | 0/7 · 0/7 | 0/12 | 0.20 | 3/6 | 3.83 · 1.17 |
| PersFormer, f=MoGe, H=1.3 fixed | 24/36 · 12/36 | 1/8 · 2/…
00:14 · asked<task-notification>
<task-id>a0643b7a4afc163b9</task-id>
<tool-use-id>toolu_0168knXLjguy5yAnWTc4eCBw</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a0643b7a4afc163b9.output</output-file>
<status>completed</status>
<summary>Agent "3D lane model on pod with estimated camera" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: PersFormer (OpenLane v1.2) run on the 12 probe clips with our per-clip camera model, scored on lane-width plausibility and back-projected paint support; pod terminated.
**Files**: `runs/lanes/3d/{scores.md, results.json, camera_params.json, versions.txt, pod_infer.py, raw/persformer_out.json, vis/<clip>.png ×12}`, `src/faultlab/lanes_3d_score.py`. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Model that ran**: PersFormer (EfficientNet-B7, OpenLane v1.2 ckpt, 652 MB via gdown) on torch 2.8/cu128 after patching its NMS + ms_deform_attn CUDA ops and numpy-2 aliases; `load_state_dict` 0 missing / 0 unexpected. LATR (mmcv 1.5 + mmdet3d) and Anchor3DLane (mmcv-full + mmseg 0.26) were not attempted. **One approximation that matters**: PersFormer derives its anchor-offset stds and 2D anchors from the label set; with no OpenLane labels on the pod I fed it a synthetic OpenLane-format set (straight/curved lanes, cam height 2.1 m), so lateral offsets are scaled by approximate stds (0.7–2.8 m per y-step). Camera: 1920×1080 letterboxed to 1920×1280, K=[[f,0,960],[0,f,640]], pitch from horizon (far-ground check lands exactly on y_h), roll ignored, H solved from CLRe…
00:14 · repliedBoth done, pod terminated, about $0.62. The 3D model lost to our own projection on both facts it was meant to deliver.
**PersFormer with our estimated camera**, on the same 36 frames and masks as CLRerNet:
| | Ego pair on paint, clips | Line support, median | Lane width at 10 m within 2.8 to 3.8 m | Width median |
|---|---|---|---|---|
| PersFormer, MoGe focal, solved height | 1/12 | 0.25 | 3/8 | 4.53 m |
| PersFormer, GeoCalib focal | 0/12 | 0.20 | 3/6 | 3.83 m |
| CLRerNet B, same frames | 3/12 | 0.55 | | |
Switching the focal source moved the width median by 0.7 m and fixing the height moved it 0.5 m, so our ±25% camera uncertainty passes straight through into the geometry. Its lane-type head called the crossed line solid on 6 clips where gold says dashed on all of them. One confound worth naming: PersFormer derives anchor statistics from a label set, and with no OpenLane labels on the pod it got synthetic ones. That wouldn't close a 2× support gap.
**Our flat-road projection of CLRerNet lines** is the part that worked, and it needs no focal for lateral geometry.
- Camera height solved from lane width on 5 clips: 1.16 to 1.62 m, plausible dashcam mounts, with one outlier at 0.79.
- Lane width at 10 m on those clips: 3.3 to 3.6 m. Off-paint pairs read 4.1 to 5.4 m, which makes width a cheap validity test for the ego pair, better than the paint-support metric whose grader we never validated.
**What didn't move.** Ego lateral motion from the lines has the right sign on both lane-change clips but comes out at a third of the expected displacement and drifts 1 to 2 m on straight clips. Fed into the tracker's prediction, predicted lines on paint went from 2 in 100 to 1 in 105. Vision-only odometry over 3 seconds isn't a CAN substitute, and the earlier gate-only tracke…
00:37 · askedcan you update the viewer to see all the things we tried so far?
00:38 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
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</fork-boilerplate>
Your directive: You are Fork Q. Extend the lane overlay viewer (docs/lane-viewer.html, built by src/faultlab/lane_viewer_build.py from runs/lanes/viewer/<clip>.json) so Ian can see every experiment we ran on the 12 probe clips, layer by layer, on the same playable 3 s windows. Local only, /Users/liwimoi/projects/fault-lab, .venv-cv; do not modify data/; no pod. Page budget ≤ 14 MB (currently 7.67 MB); keep all existing layers and behaviour; same file path (the parent republishes to the same URL). Same host constraints as before (no external resources except the fonts stylesheet, both themes, no html/head/body tags, <title> first).
Add these layers/panels, each with its own toggle, colour and legend entry, and a one-line "what this is / result" note shown when hovering or selecting the toggle (Korean, terse):
1. Tracks + IDs (perception st…
00:38 · repliedBuilding it now. The viewer gains: perception tracks with IDs and the gold counterpart highlighted, the attention picks, a variant C facts panel with CV versus gold, depth maps with per-track meters, PersFormer's projected 3D lanes, a BEV inset from the ground projection with lane width and ego offset, and an experiment index strip at the top with each group's verdict so the page doubles as the results map. I'll republish to the same URL when it's done.
00:45 · repliedTask: extended docs/lane-viewer.html with every experiment as toggleable layers on the 12 clips' 3 s windows; rebuilt and validated.
**Files**
- `src/faultlab/lane_viewer_v2.py` (new): `--augment` writes the extra layers into `runs/lanes/viewer/<clip>.json`; `--assemble` builds the page from the v2 template. `src/faultlab/lane_viewer_build.py --assemble` now delegates to it, so both entry points produce the same page.
- `docs/lane-viewer.html`: **8.97 MB** (was 7.67), same path for the parent to republish. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Added, each with toggle, colour, legend entry and a Korean hover note**
1. 지각 트랙 · ID — merged-ID boxes on all 45 frames × 12 clips; gold counterpart in gold, detector top-1 marked (cyan dot); detector contact time as a second cyan tick on the scrubber beside gold t_c.
2. 상대 attention 픽 — attention-sum top-1 (purple, dashed) and gate pick (teal) boxes on the 4 evaluated frames (3 on 006, t_c=10). **Heatmaps skipped**: `runs/counterpart/vis/*.png` are 670–760 KB whole-clip composites, no per-frame crops small enough to embed.
3. 변형 C 사실 표 — panel with every C slot: CV value, gold, 정답/오답/유보/gold 없음 pills (rows per clip: 003 6 · 011 8 · 016 6 · 023 7 · 035 7 · 042 7 · 027 1 · 43-4 clips 0, as C produced nothing there); lights.py badge on the gold counterpart box during [t_c−3, t_c] with value and blink Hz where present.
4. 깊이 — Depth Pro and DA3 maps on the 4 frames (240×135 grey PNGs, viridis-tinted client-side, per-frame log normalisation with the metre range in a label) + per-track depth labels; VDA/DAv2 left out (budget allowed them, but two maps already fill the frame; can add if wanted).
5. PersFormer 3D 차선 — back-projected with the clip's camera (K from MoGe f, E from θ/H), lane-type label pe…
00:45 · asked<task-notification>
<task-id>a3d7091047932699e</task-id>
<tool-use-id>toolu_01Szhc7LyUohwopXoEmPBpBQ</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a3d7091047932699e.output</output-file>
<status>completed</status>
<summary>Agent "Viewer: add all experiment layers" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: extended docs/lane-viewer.html with every experiment as toggleable layers on the 12 clips' 3 s windows; rebuilt and validated.
**Files**
- `src/faultlab/lane_viewer_v2.py` (new): `--augment` writes the extra layers into `runs/lanes/viewer/<clip>.json`; `--assemble` builds the page from the v2 template. `src/faultlab/lane_viewer_build.py --assemble` now delegates to it, so both entry points produce the same page.
- `docs/lane-viewer.html`: **8.97 MB** (was 7.67), same path for the parent to republish. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Added, each with toggle, colour, legend entry and a Korean hover note**
1. 지각 트랙 · ID — merged-ID boxes on all 45 frames × 12 clips; gold counterpart in gold, detector top-1 marked (cyan dot); detector contact time as a second cyan tick on the scrubber beside gold t_c.
2. 상대 attention 픽 — attention-sum top-1 (purple, dashed) and gate pick (teal) boxes on the 4 evaluated frames (3 on 006, t_c=10). **Heatmaps skipped**: `runs/counterpart/vis/*.png` are 670–760 KB whole-clip composites, no per-frame crops small enough to embed.
3. 변형 C 사실 표 — panel with every C slot: CV value, gold, 정답/오답/유보/gold 없음 pills (rows per clip: …
00:46 · repliedUpdated at the same link: https://claude.ai/code/artifact/bf577a67-024b-408b-ba1a-f4aa72c69d3e (9 MB, self-contained).
The strip across the top is the results map: eight chips, one per experiment group, each with its verdict and the n of N behind it. Clicking a chip toggles that group's layers on the video. What each shows:
- **지각 트랙 · ID**: merged track boxes on every frame, the gold counterpart in gold, the detector's top candidate dotted in cyan, and its contact time as a second tick on the scrubber next to yours.
- **상대 attention**: the DINOv2 pick and the gate pick on the four evaluated frames, so you can see the attention landing on distant objects. Heatmaps are not embedded, the only ones on disk are whole-clip composites too large for the page.
- **변형 C 사실 표**: a panel per clip with the CV value beside gold, coloured correct, wrong or unknown, and the periodogram result as a badge on the counterpart box in the three seconds before contact.
- **깊이**: Depth Pro and DA3 maps on the four frames with per-track meters on the boxes. The other two models are left out for clarity, not size.
- **차선 2D**: the layers you already had, UFLDv2 full and band, YOLOPv2, CLRerNet A sparse and B dense.
- **차선 3D**: PersFormer's lanes projected back with the clip's camera, type label on each, on the three frames it ran.
- **평면 투영**: a BEV inset in the corner, 40 m ahead by ±8 m, with the CLRerNet lines in meters, lane width at 10 m, the solved or assumed camera height, the track ground points, and a sparkline of the ego-lane offset with the current frame marked.
- **추적**: the gated Kalman layer, solid where measured and dashed where predicted.
The clips worth starting on: 053 for the case where everything works, 011 for the lane change where the tracker predictions leave the pain…
01:12 · askedwhat is 상대 attention again? what was used? what's the purpose of it?
01:13 · replied**상대 attention** was the experiment from three days ago to answer one question: given the contact time, which of the tracked vehicles is the other party? That fact is the anchor for four modifier slots, turn signal, lead-follow, gap and contact type, and the VLM had been getting it right only 3 times in 12.
**The idea**, taken from the two papers you sent. The ViT accident paper and the JCDE 3D CNN paper both located the causal vehicle not by asking a model "who" but by reading where the network's attention or class-activation map concentrated in the frames just before impact. The hope was that a vision transformer's attention near contact would naturally sit on the car about to hit us.
**What was used.** DINOv2, a general-purpose vision transformer with accessible attention, run on four frames per clip, at 1.0, 0.5 and 0.25 seconds before contact and at contact. For each tracked box we summed the attention mass falling inside it, ranked the boxes, and compared the top pick against your labelled counterpart on all 12 clips. The accident-specific models from the papers, UString and DSTA, need a Faster R-CNN feature pipeline that wasn't feasible on the Mac, so they were never run.
**What it showed.** Size-normalized attention picked the counterpart 0 times in 12. The attention sits on small high-contrast distant objects and sky edges, which is exactly what the purple boxes in the viewer show. The unnormalized "attention sum" variant scored 7 of 12, but only because bigger boxes collect more attention, which makes it a box-area prior in disguise. A gate combining it with the detector reached 9 of 12, but that rule was chosen after seeing these clips, so it's a hypothesis, not a result.
**Why it still mattered.** Three of the misses were not ranking failures at all: the…
01:20 · askedthe reason why i asked is because it looks very promising. might be a good cv helper. the only problem i see are front hood being picked up, or hard to see when and what they are because i can only tell when they are shown as overlays. can we have a timeline? what's the best way to visually see this? for off frame label tags, I can't really tell what it's tagging.
01:21 · repliedThat's a fair read, and the two problems you name are both fixable. The hood pickup goes away by applying the ego mask before summing attention, which we never did. And the "can't tell what it's tagging" problem is a rendering issue: labels for boxes clipped at the frame edge get pushed outside with no line back to their box.
The right visualization for "when and what" is a track timeline under the video: one row per tracked vehicle, a bar where it exists, shaded by how much attention it draws on each frame, with markers where each method picks it and your gold counterpart row highlighted. To make that honest, attention has to be computed on every frame, not just the four we evaluated. It's a couple of minutes of GPU on the Mac. Building both now.
01:21 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork R. Make the counterpart-attention layer of the viewer legible over time, per Ian's feedback: (1) hood/bonnet gets picked up, (2) he can't tell when/what is picked without a timeline, (3) labels for boxes clipped at the frame edge are pushed off-frame and can't be tied to their box. Local only, /Users/liwimoi/projects/fault-lab; .venv-cv (or ~/projects/vlm2/.venv where src/faultlab/counterpart.py ran DINOv2 — check its header); do not modify data/; no pod. Viewer source: src/faultlab/lane_viewer_v2.py (template + --augment/--assemble), per-clip data runs/lanes/viewer/<clip>.json, page docs/lane-viewer.html (same path; parent republishes). Budget ≤ 14 MB (now 8.97 MB). Keep every existing layer and behaviour.
A. Compute attention on ALL 45 window frames per clip (12 clips = 540 frames), reusing src/faultlab/coun…
01:21 · repliedIn progress: attention recomputed on every frame with the hood masked out, a per-track timeline under the video with attention share, pick markers and the gold row highlighted, a per-frame heatmap layer, and labels clamped inside the frame with leader lines to clipped boxes. It will also produce the number that decides whether this is a CV helper: on how many frames the masked attention top pick equals your counterpart. I'll republish and report when it lands.
01:28 · repliedFork R: masked per-frame attention, timeline, clamped labels with leaders, heatmap layer, masked verdict — done. `docs/lane-viewer.html` rebuilt (11.22 MB), ready for the parent to republish.
**Files**: `src/faultlab/attn_window.py` (DINOv2 ViT-S/14 on all 45 window frames × 12 clips, ego mask applied to the attention grid before renormalising, per-track sum/mean/area/share, per-frame top-1, 160×90 16-level heatmap PNGs ≈ 3.5 KB each), `runs/counterpart/scores_masked.{md,json}`, `runs/lanes/viewer/<clip>.json` (+`attf` per frame, `attf_meta` per clip), `src/faultlab/lane_viewer_v2.py` (template + assemble verdict). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Masked vs unmasked, 4 evaluated frames** (10 clips scorable; on 008 and 016 the gold track has no box anywhere in the 3 s window, so they are excluded):
| variant | top-1 = gold |
|---|---|
| sum, raw | 5/10 |
| sum, masked | **7/10** |
| mean (size-normalised), raw / masked | 0/10 / 0/10 |
The mask flips 042 (V13→V12 = gold) and 053 (V65→V16 = gold): large near-field boxes that included the bonnet were winning the sum. Mean is unchanged — attention still sits on distant high-contrast objects, visible in the heatmap layer.
**All-frame agreement, top-1 = gold on frames where gold has a box (the "CV helper" number)**: sum **169/302**, mean **13/302**. Per clip (sum): 017 45/45, 027 22/41, 058 20/34, 035 18/24, 011 15/18, 053 14/35, 042 12/28, 023 10/12, 006 9/27, 003 4/38. It tracks the counterpart once it is large and close (after entry), and misses it while it is small — an area-weighted prior with a hood fix, consistent with the earlier verdict.
**Viewer changes**
- Timeline panel under the video (toggle "attention 타임라인", on by default when tracks/attention/heatmap are on): one row …
01:28 · asked<task-notification>
<task-id>a78717e95c57b3878</task-id>
<tool-use-id>toolu_01NG4eiDzf8oSieCFbzSMgYN</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a78717e95c57b3878.output</output-file>
<status>completed</status>
<summary>Agent "Attention timeline + label leaders in viewer" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Fork R: masked per-frame attention, timeline, clamped labels with leaders, heatmap layer, masked verdict — done. `docs/lane-viewer.html` rebuilt (11.22 MB), ready for the parent to republish.
**Files**: `src/faultlab/attn_window.py` (DINOv2 ViT-S/14 on all 45 window frames × 12 clips, ego mask applied to the attention grid before renormalising, per-track sum/mean/area/share, per-frame top-1, 160×90 16-level heatmap PNGs ≈ 3.5 KB each), `runs/counterpart/scores_masked.{md,json}`, `runs/lanes/viewer/<clip>.json` (+`attf` per frame, `attf_meta` per clip), `src/faultlab/lane_viewer_v2.py` (template + assemble verdict). Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Masked vs unmasked, 4 evaluated frames** (10 clips scorable; on 008 and 016 the gold track has no box anywhere in the 3 s window, so they are excluded):
| variant | top-1 = gold |
|---|---|
| sum, raw | 5/10 |
| sum, masked | **7/10** |
| mean (size-normalised), raw / masked | 0/10 / 0/10 |
The mask flips 042 (V13→V12 = gold) and 053 (V65→V16 = gold): large near-field boxes that included the bonnet were winning the sum. Mean is unchanged — attention still sits on distant high-contrast objects, vis…
01:29 · repliedRepublished at the same link: https://claude.ai/code/artifact/bf577a67-024b-408b-ba1a-f4aa72c69d3e (11 MB).
**What changed in the viewer**
- **Timeline** under the video: one row per track, shaded by attention share per frame, a triangle where that track is the frame's top pick, a square for the gate pick, your counterpart's row outlined in gold, the detector's candidate dotted. Hover a cell for the numbers, click a row label to isolate that track on the video, click a cell to seek. A radio switches between sum and size-normalized mean.
- **Heatmap layer** on every frame, purple ramp, so you can see where attention actually sits.
- **Labels stay inside the frame.** Clipped boxes get a dashed leader line to the nearest visible corner, on every layer that draws labels.
- The attention pick now draws on all 45 frames, not just four.
**Your hood instinct was right, and it changed the number.** Masking the ego region before summing attention flipped two clips, 042 and 053, where a big near-field box that included the bonnet had been winning.
| Top pick equals your counterpart | Before mask | After mask |
|---|---|---|
| Four evaluated frames, 10 scorable clips | 5/10 | 7/10 |
| All frames where the counterpart has a box, 302 | | 169/302 |
| Size-normalized mean, either way | 0/10 | 0/10 |
**How to read it as a CV helper.** The per-clip pattern is the tell: 017 at 45 of 45, 023 at 10 of 12, 011 at 15 of 18, versus 003 at 4 of 38 and 006 at 9 of 27. It locks on once the counterpart is large and close, and misses it while it is still small or entering. So it is an area-weighted prior with the hood fixed, useful for ranking candidates in the last second before contact, not for the initial identification. The heatmap makes that visible: the size-normalized attention still si…
01:34 · askedi still have hard time understanding the attention. what's heatmap? what's shown on timeline? when does it have a bbox and not?
01:35 · repliedLet me take it in the order the data is made.
**Attention, what it is.** DINOv2 is a vision transformer: it cuts the frame into a grid of 14×14 pixel patches and, in its last layer, has one summary token that decides how much to "look at" each patch. Those per-patch weights are the attention. High weight means the model considered that patch important for describing the image. It was trained with no notion of accidents, cars or roads, which is why it likes anything high-contrast.
**Heatmap.** That grid of weights painted over the frame. Purple where attention is high, transparent where it is low. It's the raw signal before any decision about vehicles. When you see purple on the sky edge or a distant sign, that's the model's actual interest, not a bug.
**Bounding boxes.** These do not come from attention. They come from the perception stack: YOLO detects vehicles, ByteTrack links them across frames into tracks with IDs. A vehicle has a box on a frame only if the detector found it there and the tracker kept its ID. A box disappears when the car is occluded, leaves the frame, is too close and fills the view, or when the tracker loses it and restarts it under a new ID. That last case is the re-identification problem: on 008 and 016 your counterpart never has a box inside the 3-second window, and on 023 its track ends 1.2 s before contact and a different ID takes over.
**Putting them together.** For each frame and each box, we add up the attention inside the box. The box with the most is the frame's "attention pick." Dividing by the box's area gives the mean variant, which asks whether the model looks at that car more intensely than at other things, and that one is 0 of 10.
**Timeline.** Each row is one track ID, spanning left to right over the 3 seconds. The bar exists…
01:46 · askedsome of it looks promising as the sum goes up all the way up to 30-40 percent. depending on the severity of the collision (small bumps) don't go up by that much 10-15%. but still seems meaningful. and as you said since around the collision the bbox of vehicles are lost, for this to work well, we have to solve the bbox being lost around the collision first. all in all, i think per frame we have sums. if we integrate the sums per frame around the accident or the entire clip we can rank and identify the other party. do you agree? or am i missing something.
01:46 · repliedMostly yes, with one check I'd want before agreeing, and the data to run it is already on disk. The check: attention sum is area-weighted, so an integral of sums may just be an integral of box areas. If "who accumulated the most box area near contact" ranks the counterpart equally well, then the attention isn't adding anything and we should say so. Running both now.
01:48 · repliedRan it. The integration idea works as a ranking prior, but the ingredient doing the work is box area, not attention.
| Top pick equals your counterpart, 10 scorable clips | Integrated attention sum | Integrated box area | Integrated attention mean |
|---|---|---|---|
| Whole 3 s window | 6/10 | 6/10 | 0/10 |
| 1.5 s before contact | 6/10 | 6/10 | 0/10 |
| 1.5 s before to 0.5 s after | 4/10 | 6/10 | 0/10 |
The picks are identical on almost every clip. The 30 to 40% share you see rising is the counterpart's box filling more of the frame as it closes, which is real and useful, but it's geometry the tracker already gives us for free. The size-normalized attention, the only version that would say "the model finds this car interesting rather than merely big", is 0 for 10 in every window.
**So I agree on the mechanism and disagree on the label.** Integrate apparent size over the pre-contact window and rank: yes, that's a sensible prior, and 6 of 10 is where it lands today. Calling it attention doesn't add accuracy, and dropping DINOv2 removes a 0.1 s per frame model from the pipeline.
**What the four misses say about what's missing.**
- 003 and 053: a large bystander wins, a parked car or a lead vehicle that's big without being involved. Size alone can't separate "big" from "approaching". The detector's proximity signal, growth toward the ego over six frames, is the fix, and fusing it with the area integral is the experiment I'd run next.
- 011 and 042: the counterpart's track is short or split, so its integral is tiny even though the pick is obvious by eye. That's the re-identification point you made, and it comes first. On 008 and 016 the counterpart never has a box in the window at all.
**One caution on severity.** A bigger share at contact means a bigger box at conta…
01:53 · askedlet's go ahead and see that. plus can you keep creating docx on what we've tested, what we've found and place it on desktop with captures, explanations for even non-tech readers could understand. IN KOREAN.
01:54 · repliedTwo forks: the ranking experiment, and a Korean report series on your Desktop for non-technical readers, built as a generator so it can keep growing as we test more.
01:54 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork S. Test Ian's counterpart-ranking idea without the transformer: rank tracks by integrated apparent size over the pre-contact window, weighted by approach dynamics, and fuse with the detector's proximity candidates. Local only, /Users/liwimoi/projects/fault-lab, .venv (plain numpy is enough); do not modify data/; no pod. Code: src/faultlab/counterpart_rank.py; outputs runs/counterpart/rank/{scores.md,results.json,vis/<clip>.png}.
Inputs per clip: data/percept/<clip>/tracks.json (bbox 0–1000, per frame), id_map.json (raw→merged ids; rank MERGED ids), candidates.json (detector contact candidates with t, jerk_z, track_id (raw), event closing/exit, score; jolt_only), motion.json (per-frame flow / jerk z), gold counterpart = data/stage0_gold/<clip>.json counterpart.track_id and t_first_contact; detector t_c = top ca…
01:55 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork T. Build a Korean .docx report series for NON-TECHNICAL readers (KB 담당자·경영진) covering everything fault-lab has tested so far, with captured images and plain explanations, placed on Ian's Desktop. Also build it as a generator so future experiments can be appended. Do not modify data/; do not import from ~/projects/cv (write your own python-docx code; `pip install python-docx` into .venv if missing, plus Pillow). Work in /Users/liwimoi/projects/fault-lab; the generator is src/faultlab/report_docx.py with report content as Python data / markdown-ish sections in docs/reports/*.md (one file per topic, so appending a new experiment = adding one file). Output directory: /Users/liwimoi/Desktop/fault-lab-보고서/ (create it) — one .docx per topic plus 00-요약.docx, and an images/ subfolder with the captures used (copied from …
01:55 · repliedBoth are running. The ranking experiment scores integrated size, growth, and their fusion with the detector's proximity candidates against your 12 counterpart labels, with a failure taxonomy so we can see how many misses are track identity versus a big bystander. The report series is being generated from a set of per-topic source files, so each future experiment becomes one more file and a regenerate. I've saved that convention so it carries into later sessions. Files will land in `~/Desktop/fault-lab-보고서/`.
01:57 · repliedTask: rank tracks by integrated bbox size and approach dynamics over the pre-contact window, fused with detector candidates, scored against the 12 gold counterparts. Done.
**Files**: `src/faultlab/counterpart_rank.py`, `runs/counterpart/rank/{scores.md, results.json, vis/<clip>.png ×12}` (area-over-time curves, gold highlighted, window shaded, t_det/t_c lines). Nothing under `data/` touched; no pod; nothing committed.
**Scores (top-1 · top-3, n = clips where gold has a box in the window)**
| window | n | area_int | growth | area×growth | bottom_band | proximity (detector) | borda | product | gate |
|---|---|---|---|---|---|---|---|---|---|
| det −1.5 s | 9 | 4 · 6 | 5 · 5 | 5 · 7 | 4 · 6 | 5 · 5 | 5 · 6 | 6 · 6 | 5 · 6 |
| det −2.5 s | 9 | 3 · 6 | 5 · 6 | 5 · 6 | 3 · 6 | 5 · 5 | 3 · 6 | 5 · 7 | 4 · 6 |
| det −1.0/+0.5 s | 8 | 4 · 6 | 5 · 5 | 5 · 6 | 4 · 6 | 5 · 5 | 4 · 6 | 5 · 6 | 5 · 6 |
| **gold −1.5 s** | 10 | **8 · 9** | 7 · 9 | **8 · 9** | 8 · 9 | 5 · 5 | 8 · 9 | 8 · 9 | 8 · 9 |
Min-alive-frames 5 vs 1: no change. Raw-vs-merged variant not runnable (tracks.json stores merged tracks only).
**Verdict**: with the detector's contact time as anchor, integrated size + growth reaches 5–6/9, no better than the detector's own 6/12 or Fork D's area-at-t_c 5/12. With the gold contact time as anchor the same rankers reach 8/10 top-1, 9/10 top-3. The gap is entirely anchor error: 006 (t_det off by 8.7 s, no proximity candidate), 017 (4.5 s), 003, 053 have their window miss the counterpart. Failures remaining under the gold anchor are all track identity: 008 and 016 never boxed, 011 only 4 frames in window, 042 gold V12 split into V107. Bystander-wins-by-size happens on 0 clips once the anchor is right. So Ian's mechanism works; its two prerequisites are contact-time detect…
01:57 · asked<task-notification>
<task-id>a82d5ff25c300d5b2</task-id>
<tool-use-id>toolu_01BawFwYeDbcCVcbEJe9nVmZ</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a82d5ff25c300d5b2.output</output-file>
<status>completed</status>
<summary>Agent "Integrated area × growth counterpart ranking" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: rank tracks by integrated bbox size and approach dynamics over the pre-contact window, fused with detector candidates, scored against the 12 gold counterparts. Done.
**Files**: `src/faultlab/counterpart_rank.py`, `runs/counterpart/rank/{scores.md, results.json, vis/<clip>.png ×12}` (area-over-time curves, gold highlighted, window shaded, t_det/t_c lines). Nothing under `data/` touched; no pod; nothing committed.
**Scores (top-1 · top-3, n = clips where gold has a box in the window)**
| window | n | area_int | growth | area×growth | bottom_band | proximity (detector) | borda | product | gate |
|---|---|---|---|---|---|---|---|---|---|
| det −1.5 s | 9 | 4 · 6 | 5 · 5 | 5 · 7 | 4 · 6 | 5 · 5 | 5 · 6 | 6 · 6 | 5 · 6 |
| det −2.5 s | 9 | 3 · 6 | 5 · 6 | 5 · 6 | 3 · 6 | 5 · 5 | 3 · 6 | 5 · 7 | 4 · 6 |
| det −1.0/+0.5 s | 8 | 4 · 6 | 5 · 5 | 5 · 6 | 4 · 6 | 5 · 5 | 4 · 6 | 5 · 6 | 5 · 6 |
| **gold −1.5 s** | 10 | **8 · 9** | 7 · 9 | **8 · 9** | 8 · 9 | 5 · 5 | 8 · 9 | 8 · 9 | 8 · 9 |
Min-alive-frames 5 vs 1: no change. Raw-vs-merged variant not runnable (tracks.json stores merged tracks only).
**Verdict**: with the detector's contact time as anchor, integrated size + growth re…
01:57 · repliedThe ranking result is in, and it vindicates your idea with one condition attached.
| Anchor for the 1.5 s window | Integrated size, top-1 | Integrated size × growth, top-1 | Detector's own pick |
|---|---|---|---|
| Detector's contact time | 4/9 | 5/9 | 5/9 |
| Your labelled contact time | 8/10 | 8/10 | 5/5 |
Same rankers, same tracks. The only difference is where the window sits. When the contact time is right, integrated size picks the counterpart on 8 of 10 clips and has it in the top 3 on 9 of 10, and a big bystander wins on none. The two remaining misses are track identity: 011 has the counterpart boxed for only four frames, 042's gold track splits into a second ID. Plus 008 and 016 never boxed at all.
So the mechanism works, and its prerequisites are exactly two: the contact-time detector missing badly on some clips, 006 by 8.7 s and 017 by 4.5 s with no proximity candidate at all, and re-identification within a second of contact. Both are on the perception side, and the second is what your cv pipeline's linking stage targets. The Korean report series is still generating; I'll include this in it.
02:02 · repliedTask: Korean non-technical .docx report series (00–07) with captures and a reusable generator, on the Desktop. Done.
**Output** `/Users/liwimoi/Desktop/fault-lab-보고서/` (8 files + `images/` with the 8 captures used):
| 파일 | 크기 | 표 | 그림 | 대략 쪽수 |
|---|---|---|---|---|
| 00-요약.docx | 106 KB | 2 | 1 (파이프라인 흐름도, PIL로 그림) | 2 |
| 01-가감산-논증.docx | 41 KB | 6 | 0 | 2 |
| 02-KNIA-API-검색.docx | 39 KB | 2 | 0 | 1 |
| 03-상대차량-식별.docx | 2.3 MB | 4 | 2 (011 접촉 프레임 SoM 캡처, attention 지도) | 2 |
| 04-결정론-CV-사실.docx | 39 KB | 3 | 0 | 1 |
| 05-단안-깊이.docx | 2.1 MB | 4 | 1 (042 Depth Pro) | 2 |
| 06-차선-검출.docx | 6.8 MB | 4 | 4 (Vistas 페인트, CLRerNet 콘택트 시트, 보닛 마스크 전후 캡처, 053 평면 투영) | 3 |
| 07-데모-권고.docx | 39 KB | 3 | 0 | 1 |
All reload cleanly with python-docx; A4, Apple SD Gothic Neo 10.5pt, shaded table headers, numbered 「그림 N.」 captions, page numbers, each starting with a 「한 줄 요약」 box, terms glossed on first use (VLM, LLM, 트랙, 앵커, gold, 자차 쌍, 페인트 위, attention).
**Generator**: `src/faultlab/report_docx.py` — `python -m faultlab.report_docx [--out DIR] [--only 03]`. Content lives in `docs/reports/NN-*.md` (markdown subset: `#`, `##`, `> 요약`, `-`, `|표|` + `^ 캡션`, `{폭cm}`); adding an experiment = adding one .md and rerunning. New captures made with ffmpeg/PIL are in `runs/report_figs/` (pipeline_flow, 011_tc_som, 011_hoodmask).
**Not sourced / notes**: `runs/counterpart/rank/` (Fork S) did not exist yet, so 03 has a 「진행 중」 placeholder section. Page counts are estimates (chars/1300 + figures), all under the 6-page cap. The docx reports have one fewer figure than ideal in 01/02/04/07 (no vis assets exist for those topics; tables carry the content). Nothing under `data/` or `~/projects/cv` touched; `.venv` gained python-docx.
02:02 · asked<task-notification>
<task-id>abcab4d7dbad94afe</task-id>
<tool-use-id>toolu_016R33UMADj1EGSuVmgxjRAb</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/abcab4d7dbad94afe.output</output-file>
<status>completed</status>
<summary>Agent "Korean docx report series on Desktop" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: Korean non-technical .docx report series (00–07) with captures and a reusable generator, on the Desktop. Done.
**Output** `/Users/liwimoi/Desktop/fault-lab-보고서/` (8 files + `images/` with the 8 captures used):
| 파일 | 크기 | 표 | 그림 | 대략 쪽수 |
|---|---|---|---|---|
| 00-요약.docx | 106 KB | 2 | 1 (파이프라인 흐름도, PIL로 그림) | 2 |
| 01-가감산-논증.docx | 41 KB | 6 | 0 | 2 |
| 02-KNIA-API-검색.docx | 39 KB | 2 | 0 | 1 |
| 03-상대차량-식별.docx | 2.3 MB | 4 | 2 (011 접촉 프레임 SoM 캡처, attention 지도) | 2 |
| 04-결정론-CV-사실.docx | 39 KB | 3 | 0 | 1 |
| 05-단안-깊이.docx | 2.1 MB | 4 | 1 (042 Depth Pro) | 2 |
| 06-차선-검출.docx | 6.8 MB | 4 | 4 (Vistas 페인트, CLRerNet 콘택트 시트, 보닛 마스크 전후 캡처, 053 평면 투영) | 3 |
| 07-데모-권고.docx | 39 KB | 3 | 0 | 1 |
All reload cleanly with python-docx; A4, Apple SD Gothic Neo 10.5pt, shaded table headers, numbered 「그림 N.」 captions, page numbers, each starting with a 「한 줄 요약」 box, terms glossed on first use (VLM, LLM, 트랙, 앵커, gold, 자차 쌍, 페인트 위, attention).
**Generator**: `src/faultlab/report_docx.py` — `python -m faultlab.report_docx [--out DIR] [--only 03]`. Content lives in `docs/reports/NN-*.md` (markdown subset: `#`, `##`, `> 요약`, `-`, `|표|` + `^ 캡션`, `{폭cm}`); adding an experiment = adding …
02:02 · repliedThe report series is on your Desktop, but report 03 was written before the ranking result landed and has a placeholder. Filling it in and regenerating.
02:03 · repliedBoth done.
**Report series** is at `~/Desktop/fault-lab-보고서/`, eight Korean .docx files plus an images folder:
| File | What it covers |
|---|---|
| 00-요약 | The lab's purpose, a pipeline diagram with the human intervention points, one table of every experiment with its verdict, the 9/14 recommendation |
| 01-가감산-논증 | The oracle argument results, a full 근거 시트 for 011 as tables, the VLM fact-extraction result |
| 02-KNIA-API-검색 | Your API as retrieval, what it changed |
| 03-상대차량-식별 | Attention raw and masked, the size-integral ranking, the re-identification diagnosis |
| 04-결정론-CV-사실 | Indicator detector 2 of 2 versus VLM 0 of 3, the ego-stop failure |
| 05-단안-깊이 | Four depth models, the dash check, why meters are hard from one camera |
| 06-차선-검출 | Hood mask, model survey, CLRerNet, band crop, tracker, 3D lanes, flat-road projection |
| 07-데모-권고 | What to show, what not to claim, open questions for KB |
Each opens with a one-paragraph plain summary, glosses every technical term on first use, and puts every number in a table as n of N. Content lives in `docs/reports/*.md`, so a new experiment is one more file and a regenerate with `python -m faultlab.report_docx`. I've saved that as a standing convention.
**The ranking experiment**, now in report 03 and in the summary table: integrated box size over the 1.5 s before contact picks your counterpart on 8 of 10 clips and has it in the top 3 on 9 of 10, with no bystander ever winning, but only when the contact time is right. With the detector's contact time it drops to 5 of 9, because the detector misses 006 by 8.7 s and 017 by 4.5 s. The remaining misses are all track identity. So your mechanism stands, and its two prerequisites are contact-time detection and re-identification near contact, both perception tasks, the sec…
02:26 · askedlooking at what what we have learned and what the cv project has gathered. what do you recommend as what we should do with cv? also this was a pipeline for vlm, and the rule engine, but when and where to use each how to design the whole architecture with ui/ux for adjuster is all moving right now. how should i proceed from here?
02:27 · repliedHere's where I land, taking the four days of results and what the cv project already is.
**What to do with cv**
Make it the anchor stage and nothing more. The evidence says the whole fact pipeline hinges on two anchors, and cv is already built for exactly those.
- **Vehicle identity that survives contact.** Your gate, track, link stages are the re-identification we kept hitting. Its first acceptance test should be the 12 stage0 clips: linked ID versus your counterpart label. The scorer exists.
- **Contact time.** Fuse the jerk detector with proximity events and, once linking works, with the linked track's approach. The size-integral ranking goes from 5 of 9 to 8 of 10 the moment the window sits in the right place, so this is the highest-leverage number in the whole stack.
- **Counterpart ranking** on linked tracks: integrated size over the 1.5 s before contact. No transformer.
- **Two fact modules, no more**: the indicator periodogram on the counterpart, which beat the VLM 2 to 0, and stopped-state from road-band optical flow with the ego mask applied, which is the redesign the failed version needs.
- **Stop or park**: lanes beyond CLRerNet with the band crop and lane-width validity, monocular depth, 3D lanes, attention, predictive lane tracking. Each has a measured ceiling now.
- One contract for every module: slot, value, evidence span, confidence or abstain. Abstain beats guess, every time.
**When and where each component runs**
The roles sorted themselves out through the experiments, and they're narrower than the original design.
- **CV** runs first, always: ego mask, tracks, contact time, counterpart, the two fact modules. It anchors everything after it.
- **VLM** is not a fact source for modifiers. It hallucinates signals and answers base rates. It is good a…
02:27 · repliedReport 08 is now on your Desktop alongside the others, with the same content in Korean for the stakeholder audience.
03:46 · askedwhat is the two fact modules?
03:46 · repliedThe two deterministic modules that answer a modifier fact directly from pixels, without a VLM, and that earned a place by the numbers.
**1. Turn signal on the counterpart.** The one from new-knia's `lights.py`. It takes the counterpart's tracked box over the three seconds before contact, watches the brightness of the rear-lamp region over time, and runs a frequency analysis. A blinker in Korea is legally 1 to 2 Hz, so a peak in that band means an indicator is on. Both lamps pulsing in phase means hazards, both stepping up together means brake lights. It answers 신호불이행, the +10 modifier on 차43-2, 43-3 and 47-1. On the two clips with enough frames it was right both times, where the VLM was wrong on all three. On 042 it refused because the two lamps blinked in anti-phase, a real indicator plus a reflection, so the refusal rule needs relaxing.
**2. Stopped before the maneuver, for the ego car.** The fact behind 정체차로변경 +10 and 정차후 출발 +20. The version we tested used the median optical flow over the whole lower frame and failed on 011, because moving traffic in the band made a stopped car look like it was moving. The redesign is to apply the ego mask and restrict the flow to road pixels between the hood and the horizon, so what's measured is the road sliding past, which is zero only when the car is stopped. It hasn't been built yet in that form, which is why the roadmap lists it as a redesign rather than a result.
Everything else in the fact table is either an anchor from cv, contact time and counterpart, a VLM draft the human confirms, or a human field.
03:49 · askedone thing before we move on. would sam3 be more accurate on outlining vehicles? can that be put behind bytetrack and ReID? and what about lanes?
03:49 · repliedProbably yes on vehicles, worth testing on lanes, and it may solve a problem we've been carrying silently: the unvalidated grader.
**What SAM 3 is, briefly.** Meta's late-2025 model. You give it a concept, "car", "bus", "dashed lane line", as text or an example box, and it finds every instance, outlines each with a pixel mask, and tracks them through video with a memory of what each object looked like. So it is detector, segmenter and tracker in one, promptable with words.
**Vehicles, and behind ByteTrack and ReID.** Two concrete gains. First, masks instead of boxes: the failures we keep hitting at contact are degenerate boxes, a car filling half the frame gets a box that includes road, hood and the other car, and new-knia measured 30 to 55% of confident counterpart calls as degenerate. A mask stays on the car's pixels, so the ReID crop is cleaner and the size-integral is honest. Second, SAM 3 tracks by appearance memory, not by box overlap, so it should hold an identity through the partial occlusion and scale explosion at contact where ByteTrack splits IDs. It can sit in front of your linking stage as the detector-tracker, or the masks can feed the existing BoT-SORT and ReID as better inputs. The open-vocabulary prompt also helps with the Korean vehicle types COCO detectors are weak on.
**Lanes.** This is the Grounded-SAM route from the survey, now native. Prompts like "dashed white lane line", "solid line", "hatched zone", "stop line", "crosswalk" would give marking masks with types, which no public lane model provides. Two cautions: mask models have historically been weak on thin dashed structures, and SAM 3 improved on small objects but I haven't seen it on lane paint. And it produces paint regions, not lane geometry, so CLRerNet still supplies the line and SAM 3…
03:50 · askedgo ahead and let me see the results on the viewer too
03:50 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork U. Run SAM 3 (Meta, Nov 2025: promptable concept segmentation + video tracking) on the 12 probe clips for three questions, score them, and add the results as viewer layers. Local work in /Users/liwimoi/projects/fault-lab only; do not modify data/; do not touch ~/projects/cv. Licence irrelevant.
Pod: `7dnm4qlupn90pb` (RTX 4090, runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404, 22/tcp, your key). ip/port via mcp__plugin_runpod_runpod__get-pod (poll ~30 s until runtime.ports has 22); proxy `ssh 7dnm4qlupn90pb-644118b9@ssh.runpod.io` meanwhile. Pod pip needs PIP_BREAK_SYSTEM_PACKAGES=1. Upload is slow (25–400 KB/s): send the 12 window clips (runs/lanes/viewer/<clip>.json has the window t0; re-cut from data/videos at 1280×720 15 fps, ~3 s each, H.264 CRF 23 → ~12 × 0.5 MB) rather than frames. **Terminate the pod whe…
03:50 · repliedPod is up (`7dnm4qlupn90pb`, RTX 4090) and the run is underway: SAM 3 video tracking on the 12 windows for vehicle masks and identities, marking prompts for dashed, solid, hatched, crosswalk and stop line, three scorings against what we have, then three new viewer layers plus a SAM 3 row group in the timeline so ID switches are visible next to ByteTrack's. The fork terminates the pod when it finishes. I'll republish and report.
04:17 · asked[Image: original 2560x1110, displayed at 2000x867. Multiply coordinates by 1.28 to map to original image.]
04:19 · repliedTask: SAM 3 on the 12 probe clips (vehicles, identity, lanes), scored, added to the viewer, pod terminated. Done.
**What ran**: Meta's official `sam3` 0.1.4 package with the original `sam3.pt` checkpoint from the ungated `1038lab/sam3` mirror (facebook/sam3 is gated; the AEmotionStudio mirror is in original naming and does not load in transformers 5.17, whose `Sam3VideoModel` then outputs nothing). CLIP BPE from the OpenAI repo. Video: one text concept "vehicle" per 45-frame window at 1280×720, ~3 fps on the 4090 (8–28 s per clip). Image: 7 marking prompts every 3rd frame. Raw in `runs/sam3/raw/`, script `runs/sam3/pod_infer.py`, scorer `src/faultlab/sam3_score.py`, viewer layers `src/faultlab/sam3_viewer.py`, report `runs/sam3/scores.md`, vis `runs/sam3/vis/{clip}.png` ×12 + `lane_sheet.png`.
**Verdicts (n/N)**
1. Mask tightness vs YOLO boxes, last 1 s before contact, 87 gold-box frames: degenerate percept 10/87 vs SAM 9/87, area ratio median 0.95–1.25 → no tightness gain. The real gain: on 008 and 016, where percept never boxed the counterpart, SAM 3 segments it with one id (008 bus S0:14 in 24/25 window frames, 016 S0:14 in 10, both alive at t_c+0.5). That was a detection-recall failure, not a re-ID one.
2. Identity through contact [t_c−1.5, t_c+0.5]: matched SAM id alive at t_c+0.5 on 5/10 clips vs percept 7/10; one id switch on 4/10 (003, 027, 042, 053). No evidence SAM 3's tracker holds identity better than ByteTrack+ReID on scorable clips. Size-integral ranking on SAM tracks: gold t_c 5/10, detector t_c 4/8 (percept: 8/10, 5/9) — "vehicle" catches parked/oncoming cars too (4–17 objects/frame) and static large ones win the integral.
3. Lanes: SAM 3 line masks are thinner and stricter than Vistas (they cover 34–83% of Vistas paint; 58–95% of SAM paint lies insid…
04:19 · asked<task-notification>
<task-id>a79ee7a25fd86ba59</task-id>
<tool-use-id>toolu_01QYdniLtE9cjX85Zp4ddpjz</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a79ee7a25fd86ba59.output</output-file>
<status>completed</status>
<summary>Agent "SAM 3 vehicles + lanes on pod, viewer layers" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: SAM 3 on the 12 probe clips (vehicles, identity, lanes), scored, added to the viewer, pod terminated. Done.
**What ran**: Meta's official `sam3` 0.1.4 package with the original `sam3.pt` checkpoint from the ungated `1038lab/sam3` mirror (facebook/sam3 is gated; the AEmotionStudio mirror is in original naming and does not load in transformers 5.17, whose `Sam3VideoModel` then outputs nothing). CLIP BPE from the OpenAI repo. Video: one text concept "vehicle" per 45-frame window at 1280×720, ~3 fps on the 4090 (8–28 s per clip). Image: 7 marking prompts every 3rd frame. Raw in `runs/sam3/raw/`, script `runs/sam3/pod_infer.py`, scorer `src/faultlab/sam3_score.py`, viewer layers `src/faultlab/sam3_viewer.py`, report `runs/sam3/scores.md`, vis `runs/sam3/vis/{clip}.png` ×12 + `lane_sheet.png`.
**Verdicts (n/N)**
1. Mask tightness vs YOLO boxes, last 1 s before contact, 87 gold-box frames: degenerate percept 10/87 vs SAM 9/87, area ratio median 0.95–1.25 → no tightness gain. The real gain: on 008 and 016, where percept never boxed the counterpart, SAM 3 segments it with one id (008 bus S0:14 in 24/25 window frames, 016 S0:14 in 10, both alive at t_c+0.5). That was a detection-recall fa…
04:20 · repliedViewer republished at the same link with three SAM 3 layers, and report 09 added to the Desktop series. Pod terminated, about $0.37.
**Verdicts, in the order you asked**
| Question | Result |
|---|---|
| Tighter outlines than YOLO boxes in the last second before contact, 87 frames | Degenerate: percept 10, SAM 3 9. No gain |
| Same ID held through contact, 10 scorable clips | SAM 3 5, ByteTrack+ReID 7 |
| Counterpart ranking by size integral on SAM 3 tracks, your contact time | 5/10, versus 8/10 on percept tracks |
| Crossed-line type against gold, all dashed | 6 dashed, 1 false solid, 5 unknown, the best so far |
| Hand-picked dash rows | 1 to 5 px agreement on 006 and 053, the best dash anchor so far |
| Hatched safety zone | Fires on 0 clips, including 042 |
**The surprise is on 008 and 016.** SAM 3 found the counterpart there with a single stable ID, on 24 of 25 frames for the bus in 008. Those were the two clips where the perception stack never boxed the other car at all, which I had filed under re-identification. They were detection misses. That reframes part of the cv plan: linking can't recover a vehicle that was never detected, so a second detector on the frames where the primary abstains is worth more than I'd assumed.
**Why SAM 3 lost the ranking.** The prompt "vehicle" grabs parked and oncoming cars too, 4 to 17 objects per frame, and big static ones win a size integral. It's a prompt and filtering issue rather than a tracking one, but as-is it isn't a replacement for ByteTrack.
**On lanes, adopt it as the second opinion.** SAM 3's paint masks are thinner and stricter than Vistas. Rescoring CLRerNet's ego pair against SAM 3 paint moved on-paint frames from 19 to 10 of 83, so the "3 of 12" conclusion survives a grader change, which is the validation the …
05:13 · askedi think sam3 does a very good job honestly of segmenting it. i can see the vehicles are segmented well, another thing i noticed is its ability to detect lanes and lane markings, crosswalk, and perhaps 안전지대. does the color mean anything for these classes? i see that they are colored differently. and i'm not sure what CLRerNet uses for getting lanes, but if we can filter out only the lanes to them, (excluding other markings) it may do better.
05:13 · repliedThe colours are the prompt that produced each mask, not confidence: yellow for "dashed white lane line", red for the two solid-line prompts, purple for everything else, crosswalk, stop line, arrows, and the hatched-zone prompt. So a purple region is a marking SAM 3 classified as not-a-lane-line. One caveat: the hatched safety-zone prompt fired on zero clips, so any hatching you see in purple came through the crosswalk or arrow prompts, which means SAM 3 sees the paint but doesn't know the concept.
On your idea: CLRerNet takes the raw pixels of the road band and nothing else. It was trained on images with arrows, crosswalks and stop lines present, but our two type errors, 027's wide dash merging with an arrow and 035's bus-lane boundary, are exactly the confusions a marking filter would remove. And there's a second, more direct variant: skip the detector, and build the lane geometry straight from SAM 3's dashed and solid masks. That needs SAM 3 markings on every frame rather than every third, so it's one more pod run. Launching both variants against the same paint metric.
05:14 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork V. Test Ian's idea: use SAM 3 marking masks to (a) remove non-lane markings from CLRerNet's input, and (b) build lane geometry directly from SAM 3's dashed/solid line masks without a detector. Local in /Users/liwimoi/projects/fault-lab; do not modify data/; do not touch ~/projects/cv. Licence irrelevant.
Pod: `qgio267qs1quyq` (RTX 4090, same image as before, 22/tcp, your key). ip/port via mcp__plugin_runpod_runpod__get-pod; proxy `ssh qgio267qs1quyq-64411677@ssh.runpod.io` meanwhile. PIP_BREAK_SYSTEM_PACKAGES=1. Reuse the recipes: SAM 3 setup in runs/sam3/pod_infer.py (sam3 0.1.4, 1038lab/sam3 mirror, CLIP BPE), CLRerNet setup in runs/lanes/clrernet/versions.txt + pod_window_infer.py (mmcv 2.1.0 source build ~8 min, 3 NMS patches, numpy<2, dummy culane list). Upload only the 12 window clips (1280×720, from the…
05:14 · repliedRunning on a fresh pod: SAM 3 markings on every frame, then CLRerNet on three inputs, the original band, the band with non-lane markings painted out, and the band with only lane-line paint kept, plus a fourth route that builds the lane geometry straight from SAM 3's dashed and solid masks with no detector. All four get scored against both paint graders, Vistas and SAM 3, so we can see whether the conclusion depends on who's grading. The 027 arrow and 035 bus-lane cases get a specific look. The fork terminates the pod and republishes the viewer with the new layers.
05:23 · askedi have another question. i can see when i open up a bev viewer that it's drawing just the current state per frame not the states over time. ego is not on the viewer. some kind of real world scale might be good even if we know that the metric is not accurate. relative still works. on another note, the viewer shows lanes drawn, but an idea spins up from this is that lanes are never (unless it's being merged) narrowing or widening. meaning it should be parallel with constant width for the most part. i know this rule breaks when lanes collapse, or diverge on intersections, but for straight roads, the width of the road stays constant. maybe we can use this info to correct the bev?
05:23 · repliedBoth observations are right, and the second one is a real calibration lever, not just a sanity check.
**BEV as it stands.** It draws only the current frame, has no ego car, and the axes are in meters only nominally. Three fixes: draw the ego at the origin with a real footprint, 1.8 by 4.5 m; keep a fading trail of the last second of every track's ground position and of the lane points, so the counterpart's approach path is visible as a curve rather than a dot; and label the grid in meters with the lateral axis marked as solved from lane width and the forward axis marked as approximate, because lateral needs only camera height and horizon while forward needs the focal we never pinned down. Relative geometry stays meaningful even where the absolute scale is 25% off.
**Constant lane width as a constraint.** On a straight flat road the two lines are parallel and a fixed distance apart, which in the image means three things at once. They converge at the vanishing point, so their intersection gives the horizon row and roll without any depth model. Their pixel spacing at any row, divided by the row's distance below the horizon, is constant and equals width over camera height, so an assumed 3.25 m gives the height. And that ratio should not change from frame to frame, so any change means the fit is wrong, which is a cleaner validity test than the paint-support metric.
Some of this already exists in pieces: new-knia's lane_rigid module enforces a clip-constant width ratio and found frame-to-frame variation of 4% median, and the height solve from width is what the ground projection used. What nobody did is fit the ego pair *jointly* with those constraints, one vanishing point and one width per clip, instead of fitting each line independently and checking afterwards. A joint fi…
05:44 · askedis the work being done or just stale?
05:45 · repliedChecking rather than guessing.
05:45 · repliedLive, not stale. Verified from three angles:
- The fork is running, 30 minutes in.
- The pod is up at 31 minutes and its SAM 3 pass is done: the marking masks for all 540 frames came back at 14:29 and the local skeleton files were being written a minute ago.
- The pod's GPU is idle right now, which fits the current step: the fork is precomputing the local paint skeletons before sending the three CLRerNet input variants back up. The CLRerNet build alone takes about eight minutes, so expect another 20 to 30 minutes before scores.
If it goes quiet for more than an hour I'll check the pod again, since it bills whether or not anything is running.
06:03 · repliedTask: SAM 3 marking masks on all 540 window frames; CLRerNet B on filtered inputs (A original / B other-markings removed / C lane-lines only); SAM 3-only lane geometry (D); scored against Vistas and SAM 3 graders; viewer layers; pod terminated.
**Files**: `src/faultlab/sam3filter.py` (scoring, D geometry, vis, tracker hook, viewer augment+patch), `runs/lanes/sam3filter/{scores.md, results.json, raw/window_ABC.json, raw/<clip>_marks.json, masks/ (540 npz, 437 MB, cache), vis/<clip>.png ×12 + sheet.png, variant_samples/ (B/C inputs for 027/035/053), track/, versions.txt, pod/*.py}`. `docs/lane-viewer.html` rebuilt 13.61 MB. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Headline (540 frames; ego pair on paint ≥0.5, Vistas / SAM grader; support median Vistas)**
| variant | pair | ≥0.5 V / S | support V | type: dashed · false solid · unknown |
|---|---|---|---|---|
| A0.4 original | 260 | 60 / 31 | 0.46 | 4 · 1 · 7 |
| B0.4 other markings painted out | 259 | 39 / 19 | 0.42 | 5 · 1 · 6 |
| C0.4 lane lines only | 21 | 1 / 4 | 0.25 | 2 · 0 · 10 |
| D SAM3-only geometry | 154 | 33 / 40 | 0.35 | **7 · 0 · 5** |
- **Filtering does not help CLRerNet.** B loses on the clips where A was on paint (053 39→30, 042 10→4); C collapses (82/540 frames with any line). CLRerNet uses road context; strip it and the anchors fail. Lowering the threshold to 0.2 adds pairs but not paint. The one gain is type: 027's arrow blob goes unknown → dashed with B; 035 (bus-lane boundary) is dashed under A and B alike; the single false solid persists.
- **D is complementary, not a replacement**: it gets pairs on paint where A gets none (058 10, 027 4, 008 5, 017 lines 11) and loses 053 (6 vs 39); hood-row jitter 15.9 px vs 4.7. Its crossed-line type by the RGB ridge is the best o…
06:03 · asked<task-notification>
<task-id>a61fb69796fd3a946</task-id>
<tool-use-id>toolu_014RFd5rNP6PkNCDarPLdPXk</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a61fb69796fd3a946.output</output-file>
<status>completed</status>
<summary>Agent "SAM3 marking filter for CLRerNet + SAM3-only lane geometry" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: SAM 3 marking masks on all 540 window frames; CLRerNet B on filtered inputs (A original / B other-markings removed / C lane-lines only); SAM 3-only lane geometry (D); scored against Vistas and SAM 3 graders; viewer layers; pod terminated.
**Files**: `src/faultlab/sam3filter.py` (scoring, D geometry, vis, tracker hook, viewer augment+patch), `runs/lanes/sam3filter/{scores.md, results.json, raw/window_ABC.json, raw/<clip>_marks.json, masks/ (540 npz, 437 MB, cache), vis/<clip>.png ×12 + sheet.png, variant_samples/ (B/C inputs for 027/035/053), track/, versions.txt, pod/*.py}`. `docs/lane-viewer.html` rebuilt 13.61 MB. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Headline (540 frames; ego pair on paint ≥0.5, Vistas / SAM grader; support median Vistas)**
| variant | pair | ≥0.5 V / S | support V | type: dashed · false solid · unknown |
|---|---|---|---|---|
| A0.4 original | 260 | 60 / 31 | 0.46 | 4 · 1 · 7 |
| B0.4 other markings painted out | 259 | 39 / 19 | 0.42 | 5 · 1 · 6 |
| C0.4 lane lines only | 21 | 1 / 4 | 0.25 | 2 · 0 · 10 |
| D SAM3-only geometry | 154 | 33 / 40 | 0.35 | **7 · 0 · 5** |
- **Filtering does not help CLRe…
06:04 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork W. Rebuild the viewer's BEV inset per Ian's feedback and add a jointly constrained ego-lane fit. Local only, /Users/liwimoi/projects/fault-lab, .venv-cv (KMP_DUPLICATE_LIB_OK=TRUE); do not modify data/; no pod. Viewer source src/faultlab/lane_viewer_v2.py (+ sam3_viewer.py, sam3filter.py patches), data runs/lanes/viewer/<clip>.json, page docs/lane-viewer.html (same path; parent republishes; ≤ 14 MB, now 13.61 MB — be frugal: the BEV needs only per-frame lane params and track ground points, no images).
Part 1 — joint constrained ego-pair fit (src/faultlab/lane_joint.py): for each clip, take CLRerNet B lines (runs/lanes/clrernet/raw/window_B.json) and SAM 3 dashed/solid lane skeletons (runs/lanes/sam3filter/masks/ or the D geometry in runs/lanes/sam3filter/results.json) as candidate ego-line pixel sets per frame…
06:04 · repliedTask: joint constrained ego-pair fit per clip (shared horizon, roll, constant width ratio) scored against paint, plus BEV v2 inset in the viewer with ego, trails, metric grid.
06:04 · repliedThe filter test is done, the viewer has its two new layers at the same link, report 09 is updated, and the BEV rebuild with your constant-width idea is now running.
**Your filter idea, measured.** It hurt CLRerNet, and the reason is instructive.
| CLRerNet input, 540 frames | Ego pair on paint, Vistas grader | SAM 3 grader | Crossed-line type: dashed, false solid, unknown |
|---|---|---|---|
| A, original band | 60 | 31 | 4, 1, 7 |
| B, other markings painted out | 39 | 19 | 5, 1, 6 |
| C, lane-line paint only | 1 | 4 | 2, 0, 10 |
| D, SAM 3 masks only, no detector | 33 | 40 | 7, 0, 5 |
CLRerNet reads the whole road, curb, texture, arrows and all, and its anchors lose their footing when the context is removed. Variant C, which is what "give it only the lanes" literally means, finds a line on 82 of 540 frames. One thing did improve with B: 027's arrow blob, which had been called unknown, became dashed. And 035's bus-lane boundary was read as dashed under A and B alike.
**D is the interesting one.** Built directly from SAM 3's dashed and solid masks with no detector, it finds pairs on the clips where CLRerNet finds none, 058, 027 and 008, and loses badly on 053. Its type call by brightness rhythm is the best of the four, zero false solids. But SAM 3's own dashed-versus-solid prompt scores are unusable: it labels the same paint under both prompts. So SAM 3 finds the paint, and the rhythm along it says what kind it is.
**Both graders agree on the ranking**, A over B over D over C, even though SAM 3's paint is stricter. That's the validation the on-paint metric was missing.
**Now building**: the BEV inset with the ego car at the origin, fading trails for every track, a metric grid with the lateral axis marked as solved and the forward axis as approximate, and the joint…
06:16 · repliedTask: joint constrained ego-pair fit per clip (shared horizon, roll, constant width ratio) scored against paint, plus BEV v2 inset in the viewer. Both done; nothing committed, `data/` untouched, no pod.
**Files**: `src/faultlab/lane_joint.py` (model in docstring), `runs/lanes/joint/{scores.md, results.json, vis/<clip>.png ×9}`, `src/faultlab/lane_viewer_v2.py` (BEV v2, toggle, click-to-seek), `runs/lanes/viewer/<clip>.json` (+`joint` per clip, `jt` per frame), `docs/lane-viewer.html` rebuilt **13.72 MB** (parent republishes). Validated: only the fonts stylesheet is external, no html/head/body tags, embedded JSON parses, `node --check` ok, 9 clips carry joint data (011/016/017 have <3 frames with both sides, no fit).
**Model note**: the first version used the 1/(y−y_h) domain inherited from lane_track; image lines through a vanishing point are linear in v = y−y_h, not in 1/v, and the width criterion only became meaningful after switching (053 spread 1.1 → 0.09 at the true horizon). Lines: x = a + b·v + c·v², shared a (VP) and c per frame, b_R−b_L = Δb = W/H per clip, H = 3.25/Δb. (y_h, roll) by grid search on the parallelism cost (spread of (x_R−x_L)/v over rows), roll clamped to ±5°. Candidate per side = CLRerNet B ego line, SAM 3 geometry only when CLRerNet is absent/off paint (a union or support-only choice picked the next lane's line on 053).
**Horizon and H (9 clips)**
| joint y_h vs | median \|Δ\| | ≤ 30 px |
|---|---|---|
| flat-road fit (Fork I) | 20 px | 8/9 |
| GeoCalib (y_h = cy + f·tan pitch) | 24 px | 7/9 |
Three independent routes now agree on the horizon. H from the joint width: 053 1.27 (ground 1.30), 035 1.26 (1.16), 042 1.40 (1.62), 003 1.11 (0.79), 008 1.35 (1.03); 006 0.97, 023 0.93, 027 1.03, 058 2.29 where the pair is off paint. Width-ratio fra…
06:16 · asked<task-notification>
<task-id>a9e1c77be7cd3cf17</task-id>
<tool-use-id>toolu_017J5JDuSy74EixmgfwgP2SF</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a9e1c77be7cd3cf17.output</output-file>
<status>completed</status>
<summary>Agent "BEV v2: ego, trails, joint parallel-lane fit" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: joint constrained ego-pair fit per clip (shared horizon, roll, constant width ratio) scored against paint, plus BEV v2 inset in the viewer. Both done; nothing committed, `data/` untouched, no pod.
**Files**: `src/faultlab/lane_joint.py` (model in docstring), `runs/lanes/joint/{scores.md, results.json, vis/<clip>.png ×9}`, `src/faultlab/lane_viewer_v2.py` (BEV v2, toggle, click-to-seek), `runs/lanes/viewer/<clip>.json` (+`joint` per clip, `jt` per frame), `docs/lane-viewer.html` rebuilt **13.72 MB** (parent republishes). Validated: only the fonts stylesheet is external, no html/head/body tags, embedded JSON parses, `node --check` ok, 9 clips carry joint data (011/016/017 have <3 frames with both sides, no fit).
**Model note**: the first version used the 1/(y−y_h) domain inherited from lane_track; image lines through a vanishing point are linear in v = y−y_h, not in 1/v, and the width criterion only became meaningful after switching (053 spread 1.1 → 0.09 at the true horizon). Lines: x = a + b·v + c·v², shared a (VP) and c per frame, b_R−b_L = Δb = W/H per clip, H = 3.25/Δb. (y_h, roll) by grid search on the parallelism cost (spread of (x_R−x_L)/v over rows), roll cl…
06:17 · repliedViewer republished with "평면 투영 v2", same link. Report 06 on the Desktop has the new section.
**Your constant-width idea, measured.** It's a strong calibration check and a weak line corrector.
- **Horizon**: fitting both lines to one vanishing point gives a horizon that agrees with the depth-based flat-road fit within 20 px on 8 of 9 clips and with GeoCalib within 24 px on 7 of 9. Three independent routes now point at the same row, so camera tilt is settled.
- **Camera height** from a 3.25 m lane: 1.27, 1.26, 1.40 and 1.35 m on the clips where the pair sits on paint, all plausible mounts. Off-paint pairs give 0.93 to 2.29 m, so width constancy flags a wrong pair by itself. On 053 the width ratio varies 1.4% frame to frame.
- **But pairs on paint did not increase**: 42 of 405 frames under the joint fit against 60 for the per-frame CLRerNet pair. Coupling two lines pulls a good one toward its bad partner. The validity flag is the useful part: of the 78 frames it accepts, 41 are on paint, a much higher rate than the unfiltered 60 of 216.
So the constraint belongs in validation and calibration, not in the line fit. One implementation note: the first version used the inverse-row domain from the tracker and the width criterion only made sense after switching to plain row offset, where lines through a vanishing point are actually straight.
**BEV v2**: ego car at the origin with a heading arrow, lateral axis in meters from lane width with 1 m ticks, forward axis in approximate meters marked as such, 15-frame fading trails for every track with your counterpart in gold, the joint pair over the raw pair in three colours by status, and a readout of height, width, horizon and frame validity. Clicking a trail point seeks the video. Three clips, 011, 016 and 017, have no joint fit …
06:25 · askedBEV is still different from how i view it. although it is closer than v1, what i envisioned is more like a static map, and cars moving on it. do you understand what i mean? and that also kind of has to do with our advantage over real-time forward pass restrictions most ADAS has (yes they do have more sensors). we draw the 2D static map. the lanes, lane markings, etc all on the 2D map. the map covers entire trajectory. when we play it, the vehicles move in that 2D map. i don't know what's required to do that. brainstorm with me.
06:26 · repliedYes. You mean a reconstruction: a world-fixed top-down map of the road, lanes and markings, built once from the whole clip, and then the ego car and every other vehicle replayed on it as moving objects. The BEVs so far are ego-fixed snapshots; you want the map fixed and the cars moving. And your point about the offline advantage is the right one: ADAS must decide from frames it has already seen, we can use frames that come *later* to fix what happened earlier.
**What it takes, four pieces**
1. **Ego pose per frame in a world frame**: position, heading. This is the hard part and everything else hangs on it.
2. **The static paint map**: project each frame's lane and marking pixels onto the ground plane with that frame's pose, accumulate across the clip. Where the same dash lands in the same place from 30 frames it becomes crisp; where poses are wrong it smears. Map sharpness is the pose-accuracy meter, built in.
3. **Vehicles in the world frame**: each track's ground contact point, bbox bottom or better the SAM 3 mask bottom, through the same projection, then smoothed in world coordinates. This is where the payoff is largest: a car moving through a world map obeys physics, constant-ish velocity and smooth heading, so linking IDs through occlusion and contact becomes a motion-consistency problem instead of an appearance-matching one.
4. **Ego on the map**: the trajectory itself, with the front bumper meeting the counterpart's track at contact time as a consistency check.
**Where each piece stands**
- Ground projection per frame: done. Horizon from three agreeing routes, height from lane width, lateral in meters, forward in meters up to a constant we don't know.
- Paint per frame: SAM 3 masks on every frame, dashed, solid, crosswalk, stop line. hd-map already accumulate…
06:28 · askeduh...we don't have hd-map
06:28 · repliedSorry, ambiguous wording. I meant the `~/projects/hd-map` *project directory*, not an HD map. That repo contains, among the map-matching work, a `bev_view.py` that accumulated a top-down view of the road paint over about one second of a single clip from the camera alone, and noticed that forward motion fills in the dash gaps. That's the technique I'm pointing at, extended to the whole clip.
Nothing in the proposal needs an external map. The static map is built from the clip itself: each frame's paint projected to the ground with that frame's camera pose, stacked up over the clip. The only inputs are the video, the SAM 3 masks, the horizon and height we already solve, and the ego trajectory we'd estimate from the static scene. Where a real HD map exists, Gangnam only today, it could later be laid underneath as a reference, but the reconstruction stands without it.
06:30 · askedok. let's see how it goes
06:30 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
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</fork-boilerplate>
Your directive: You are Fork X. Prototype Ian's world-fixed map: a static top-down map of the road paint built from the whole clip, with the ego car and every tracked vehicle replayed on it. Three clips first: 053, 011, 042 (then 023 and 035 if time allows). Local work in /Users/liwimoi/projects/fault-lab (.venv-cv, KMP_DUPLICATE_LIB_OK=TRUE); do not modify data/; do not touch ~/projects/cv. Licence irrelevant. Use the FULL 10 s clip (data/videos/<clip>.mp4, 15 fps, 150 frames), not just the 3 s window — the map should cover the whole trajectory. Inputs you already have: horizon/roll/H per clip (runs/lanes/joint/results.json, runs/depth/intrinsics/fused.json, runs/lanes/ground/), f = MoGe-2 per clip (approximate; forward scale is allowed to be off by a constant), SAM 3 vehicle masks + lane/marking masks for the 3 s window (runs/sam3/raw, r…
06:30 · repliedStarted on a fresh pod (`99vm3juex8ue9i`). The prototype runs on 053, 011 and 042 over the full 10 seconds: SAM 3 masks on every frame, two ego-pose routes, lanes plus dash-phase speed and static-scene structure-from-motion with vehicles masked out, then the accumulated paint map for each route with a sharpness measure on the dash edges, vehicles replayed on the sharper map with world-frame smoothing, and a contact-consistency check. A "월드맵" panel goes into the viewer, and per-clip replay videos land under `runs/worldmap/`. This is the longest job so far, several hours. I'll report when it's done.
06:52 · asked[Image: original 321x2213, displayed at 290x2000. Multiply coordinates by 1.11 to map to original image.]
06:53 · askedwhile that is being done, can we try other approaches? since this is getting too big, let's make a new directory called bev under projects. Reconstructing a 2D Bird's-Eye-View (BEV) from an uncalibrated dashcam requires either inferring the missing camera parameters on the fly or using a calibration-free architecture that bypasses camera matrices entirely.Approach 1: Two-Stage Auto-Calibration (Best for Metric Scale)This strategy estimates approximate camera intrinsic matrix $\mathbf{K}$ and extrinsic pose $[\mathbf{R} \mid \mathbf{t}]$ directly from pixel content before executing a standard BEV transform:Intrinsics ($\mathbf{K}$): Assume zero skew and set the principal point to the image center ($c_x = W/2, c_y = H/2$). Use a monocular calibration estimator (such as WildCamera) to regress focal length $f$ or Field of View (FOV).Pitch ($\theta$) & Roll ($\phi$): Calculate the scene's primary vanishing point derived from parallel lane lines, road curbs, or building edges.Camera Height ($h$): Infer vertical offset using physical object priors—such as detecting standard vehicle wheel contact points or pedestrian heights on the road plane to act as a metric measuring stick.BEV Projection: Pass the reconstructed matrices into a standard Lift-Splat-Shoot (LSS) or Inverse Perspective Mapping (IPM) pipeline.Approach 2: Calibration-Free Direct RegressionSkip geometry matrices altogether using models built for "in-the-wild" wild-camera perception (such as UrbanOmniDetect):Mechanism: Cross-attention transformer layers map perspective image features directly to an ego-centric BEV grid by relying on learned contextual priors (e.g., standard road lane widths, typical car dimensions).Trade-off: Highly robust to unknown lenses, but metric scale accuracy degrades if the camera is mounte…
06:54 · repliedUnderstood: a new `~/projects/bev` project for the calibration-and-BEV question on its own, separate from fault-lab, with these three approaches as its first experiments. One honesty note before I start: I'll verify each named model exists with public weights before building on it. WildCamera is real, a NeurIPS 2023 monocular intrinsics estimator. "UrbanOmniDetect" and "OpenVO" I can't place, so the survey will find the actual nearest systems, for instance MASt3R-SLAM's uncalibrated mode and DPVO for approach 3, and whatever calibration-free monocular BEV model has weights for approach 2. Approach 1 overlaps with work already done in fault-lab, so it starts from those numbers rather than repeating them.
06:55 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
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</fork-boilerplate>
Your directive: You are Fork Y. Create a new project /Users/liwimoi/projects/bev for "2D BEV reconstruction from an uncalibrated dashcam" and run Ian's three approaches on the 12 fault-lab probe clips. Do not modify anything under ~/projects/fault-lab/data or ~/projects/cv; read fault-lab freely and symlink its assets. Licence irrelevant (feasibility). Another fork is currently using pod 99vm3juex8ue9i for a world-map reconstruction — do not touch that pod; create your own if you need CUDA (mcp__plugin_runpod_runpod__create-pod with body {"name":"bev-<approach>","image":"runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404","gpu":{"id":"NVIDIA GeForce RTX 4090","count":1},"cloud":"SECURE","disk":80,"ports":["22/tcp"],"startSsh":true,"env":{"PUBLIC_KEY":"ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIGUJbkaKZcZdefjZlG2fWSV/tQ0LD7k8xILkQR4BU54V ian.park@co…
06:55 · repliedTwo jobs are running in parallel now:
- **fault-lab, world-map prototype** on pod `99vm3juex8ue9i`: full-clip masks, two ego-pose routes, accumulated paint maps with a sharpness measure, vehicles replayed, a 월드맵 panel in the viewer.
- **`~/projects/bev`, new project**: directory scaffolded with the same conventions as cv, a survey that checks each named system actually exists, then approach 1 locally on the 12 clips, approach 2 and 3 on their own pod if they need CUDA. Approach 3 uses a different SLAM system from the one the world-map job uses, so the two can be compared later. Each approach ends with a Korean report under `bev/reports/`, and the engineering write-up names which of focal, pitch-roll, or height is the actual weak link, with numbers.
I've noted the split in memory so a session opened from `~/projects/bev` knows where it came from.
06:58 · repliedTask: prototype the world-fixed map (static paint map + ego/vehicle replay) on 053/011/042 + 023/035 with lane-based (S1) and VGGT (S2) ego poses, score, viewer panel, pod terminated. Done.
**Files**: `src/faultlab/worldmap.py` (S1/S2 pose, map, sharpness, vehicles, replay), `src/faultlab/worldmap_viewer.py` (panel + template patch), `runs/worldmap/<clip>/{map_stage1.png, map_stage2.png, replay_stage1.mp4, replay_stage2.mp4, poses.json, tracks_world_stage{1,2}.json, summary.json}` ×5, `runs/worldmap/scores.md` + `scores.json`, `runs/worldmap/raw/` (SAM 3 full-clip veh/marks + VGGT for 5 clips), pod scripts in `runs/worldmap/pod/`. `docs/lane-viewer.html` rebuilt 13.28 MB (parent republishes). `docs/fault-lab-v1-3.md` §14 added. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Pod** `99vm3juex8ue9i`: delete → 204, get → 404. 06:30–07:25 UTC ≈ 55 min ≈ $0.68. SAM 3 (vehicle + 7 markings, 150 frames) 2–3 min/clip; VGGT-1B 50 keyframes 4–7 s/clip. CLRerNet not run: mmcv from source was still compiling at 50 min (arch-limited build did not help), so S1 lateral/heading comes from SAM 3 dashed/solid masks instead.
**Results (5 clips, full 10 s)**
| clip | f 보정 k | H m | S1 lateral frames | S1 dash/flow | speed m/s | path m | S1 sharp cm (edges) | S2 sharp cm | S2 vs S1 lat RMS m · head RMS ° · path ratio | contact t_c percept / SAM3 m | gold SAM3 alive t_c+0.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 053 | 0.87 | 1.27 | 150/150 | 48/48 | 15.0 | 161 | 30 (362) | 20 (197) | 5.65 · 2.1 · 0.74 | 12.95 / — | no |
| 011 | 1.16 | 1.30 | 70/150 | 14/97 | 15.2 | 154 | 55 (524) | 90 (142) | 1.01 · 4.6 · 0.21 | 6.56 / 7.81 | yes |
| 042 | 1.12 | 1.40 | 134/150 | 67/44 | 18.8 | 186 | 40 (118) | 170 (116) | 2.57 · 5.5 · 0.28 | — / — | no |
| 023 | 0.9…
06:58 · asked<task-notification>
<task-id>ab318bc5e1236400b</task-id>
<tool-use-id>toolu_018Mw9ibG5AUnMSL1z9GY97g</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/ab318bc5e1236400b.output</output-file>
<status>completed</status>
<summary>Agent "World-fixed map reconstruction prototype" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: prototype the world-fixed map (static paint map + ego/vehicle replay) on 053/011/042 + 023/035 with lane-based (S1) and VGGT (S2) ego poses, score, viewer panel, pod terminated. Done.
**Files**: `src/faultlab/worldmap.py` (S1/S2 pose, map, sharpness, vehicles, replay), `src/faultlab/worldmap_viewer.py` (panel + template patch), `runs/worldmap/<clip>/{map_stage1.png, map_stage2.png, replay_stage1.mp4, replay_stage2.mp4, poses.json, tracks_world_stage{1,2}.json, summary.json}` ×5, `runs/worldmap/scores.md` + `scores.json`, `runs/worldmap/raw/` (SAM 3 full-clip veh/marks + VGGT for 5 clips), pod scripts in `runs/worldmap/pod/`. `docs/lane-viewer.html` rebuilt 13.28 MB (parent republishes). `docs/fault-lab-v1-3.md` §14 added. Nothing under `data/` or `~/projects/cv` touched; nothing committed.
**Pod** `99vm3juex8ue9i`: delete → 204, get → 404. 06:30–07:25 UTC ≈ 55 min ≈ $0.68. SAM 3 (vehicle + 7 markings, 150 frames) 2–3 min/clip; VGGT-1B 50 keyframes 4–7 s/clip. CLRerNet not run: mmcv from source was still compiling at 50 min (arch-limited build did not help), so S1 lateral/heading comes from SAM 3 dashed/solid masks instead.
**Results (5 clips, full 10 s)**
| clip | f 보정 k | H …
06:59 · repliedThe first world-fixed maps exist. Two replays and a raw map are above, the viewer has a "월드맵" panel for five clips at the same link, and report 10 is on the Desktop. Pod terminated, about $0.68.
**What came out**, five clips over the full 10 seconds:
| Clip | Dash-edge sharpness, lane-based poses | Same, VGGT poses | Ego path | Ego-to-counterpart distance at contact |
|---|---|---|---|---|
| 053 | 30 cm | 20 cm | 161 m | 12.9 m, track jumps to a far car right after impact |
| 011 | 55 cm | 90 cm | 154 m | 6.6 to 7.8 m, side contact |
| 042 | 40 cm | 170 cm | 186 m | counterpart lost before contact |
| 023 | 20 cm | 50 cm | 205 m | 2.3 m |
| 035 | 50 cm | 50 cm | 132 m | 3.2 m |
Sharpness is the built-in accuracy meter: the same dash landing in the same place from 30 frames. Two clips come out crisp at 30 cm or better. The lane-based route beat VGGT on four of five. VGGT recovers the *shape* of the trajectory in seconds, but its scale wandered between 0.2 and 0.7 of the lane-based path even after re-anchoring, which is the single-image scale problem again.
**What you'll see in the replays.** On 053, two straight dashed lines, the ego inside its lane, the counterpart closing from the left lane, and then the label jumping to a distant car after impact. On 011 the ego's own lane change shows as a one-meter lateral step in the trajectory. Both are physically sane at lane scale. The fan-shaped smear of crosswalk and arrow paint at the clip's start is the tell for the first fix: pitch changes under braking are not corrected per frame, and near-field markings are the most sensitive to that.
**Two things still block the picture you want.**
- Forward scale: dash-pitch correction brings the focal to within about 15%, and optical-flow speed reads 1.2 to 1.6 times slow, so the…
07:13 · repliedYou've reached your Fable limit. Run /usage-credits to continue or switch models with /model.
07:13 · asked<task-notification>
<task-id>a86dc906cdf814d6c</task-id>
<tool-use-id>toolu_01G13Z1N7qAkV2yoT5tGE88j</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a86dc906cdf814d6c.output</output-file>
<status>failed</status>
<summary>Agent "Create ~/projects/bev and run three calibration approaches" failed: Agent terminated early due to an API error: You've reached your Fable limit. Run /usage-credits to continue or switch models with /model. (error type rate_limit, HTTP 429, request id req_011CeuLxbDt8ETMw95LfjbEA, model sent to the API: claude-fable-5-1)</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
</task-notification>
07:13 · repliedYou've reached your Fable limit. Run /usage-credits to continue or switch models with /model.
07:36 · asked<command-name>/model</command-name>
<command-message>model</command-message>
<command-args></command-args>
07:36 · asked<local-command-stdout>Set model to `Opus 5 (1M context) (default)` and saved as your default for new sessions</local-command-stdout>
07:36 · asked<local-command-caveat>Caveat: The messages below were generated by the user while running local commands. DO NOT respond to these messages or otherwise consider them in your response unless the user explicitly asks you to.</local-command-caveat>
07:37 · repliedThe bev fork died on a rate limit mid-run. First priority is checking whether it left a pod billing, then resuming from whatever it managed to create.
07:37 · repliedThree pods exist. One is mine and orphaned by the failed fork; two named `cv-sweeps` belong to your cv-processing session, so I'll leave those alone. Checking what the fork completed before deciding whether to keep the pod.
07:37 · repliedThe fork got further than the failure suggested: survey verified, approach 1 and approach 2 both complete on all 12 clips. Only approach 3 is unrun, and its pod is sitting idle at $0.74/h. Relaunching onto that pod now.
07:38 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork Z, resuming work in /Users/liwimoi/projects/bev that a previous fork left unfinished (it died on a rate limit). Do not modify ~/projects/fault-lab/data or ~/projects/cv. Licence irrelevant.
State: the project exists (config.toml, subsets/probe-12.txt, data/ symlinks to fault-lab, .venv). `docs/survey.md` is complete and verified. Approach 1 (WildCamera focal + lane vanishing point + camera height from object priors + IPM) is DONE — `work/a1/{results.json, scores.md, wildcamera.json, vis/}`. Approach 2 (UrbanOmniDetect calibration-free vehicle BEV) is DONE — `work/a2/<clip>_bev.mp4`, `<clip>_labels.json`, logs for all 12 clips. Approach 3 (temporal motion auto-calibration) is NOT run: `work/vo/clips` has staged clips only. Read `docs/survey.md` first — it records the chosen system (MASt3R-SLAM uncalibrated, `us…
07:38 · repliedI'll resume the bev project: run Approach 3 (temporal motion auto-calibration) on the idle pod, then write the missing project files, results doc, and three Korean reports.
07:38 · repliedApproach 1 and 2 results are in hand. Now to the pod — it's billing, so priority is getting Approach 3 running.
07:38 · repliedApproach 1 and 2 are done; approach 3 is now running on the idle pod. Also, correction to what I said earlier: the survey verified both systems you named are real. OpenVO is CVPR 2026 with code released in May, and UrbanOmniDetect is on Hugging Face. OpenVO wasn't run because its pinned stack, torch 2.0.1 with CUDA 11.7 and mmcv-full 1.7.2, won't build on a current pod inside the time box. That's a container problem, not a dead end.
**Focal length.** WildCamera is the most self-consistent source, identical across frames of a clip, and it agrees with MoGe-2 on 9 of 12 clips, GeoCalib on 7, Depth Pro on 5, and Depth Anything 3 on none. But against the physical dash-length check it still passes only 1 of 4 measurable clips, same as MoGe. Agreement between estimators is not accuracy.
**Pitch and roll are solid.** The IPM lane width drifts by 0.002 m per meter of distance on clip 003, essentially perfectly parallel out to 24 m. When the horizon is right, the ground plane is right.
**Height is the weak link, and here is the proof.** On clip 003 the same lane, same frames, measured four ways:
| Camera height source | Height | Lane width it produces |
|---|---|---|
| From lane width, circular | 1.11 m | 3.42 m |
| Car-height prior | 1.67 m | 5.14 m |
| Vehicle-width prior | 2.63 m | 8.06 m |
| Assumed 1.3 m | 1.30 m | 3.99 m |
The only height that yields a plausible lane is the one derived from assuming the lane is 3.25 m, which proves nothing. The independent priors are worse: the vehicle-width prior gives plausible mounts on 1 of 11 clips, the car-height prior on 6 of 12 with a median of 1.35 m.
So your proposed recipe holds for two of three parts. Vanishing point for pitch and roll works. A light intrinsics net gives a stable focal but not a verified one. Assuming 1.3 …
07:39 · repliedPod is alive. Starting the MASt3R-SLAM build in the background immediately, since the CUDA extensions are the long pole.
07:39 · repliedInstall is building. While it does, let me check the reference data I'll compare against and write the local self-calibration baseline.
07:40 · repliedMASt3R-SLAM is at the extension build. Meanwhile I'll write the local half of Approach 3: plane-homography visual odometry, which is exactly the "ground-plane fitting solves pitch/roll/height over time" claim, plus focal-from-motion self-calibration.
07:42 · repliedNow the experiment runner for the local half.
07:43 · repliedTwo diagnostics needed: the focal estimate is degenerate as predicted, but the plane VO solved only 3 of 149 frames. Let me debug the homography selection.
07:43 · repliedFound it: OpenCV returns the road normal pointing down (+y), not up. My sign filter rejected the correct solution. Fixing.
07:44 · repliedPlane VO now solves 101 of 149 frames, but reads 3.3 m/s where the reference says 15. The thin road band is dominated by near-static far points. Filtering to points that actually move.
07:45 · repliedThe displacement filter made it worse. The real issue is geometric: a thin, far road band under forward motion is ill-conditioned for homography decomposition. The well-conditioned route is to rectify the road to a metric top-down view first, where forward motion becomes a pure translation.
07:47 · repliedNow it's clear: with a central strip and a response gate, BEV phase correlation gives 16.2 m/s against the reference 15.0, and gaps of 1, 2 and 3 frames scale linearly. My earlier zero came from the static mask wedge dominating the correlation. Rewriting the estimator properly.
07:47 · askedof course it varies largely by what the ego vehicle is (sedan, internal dashcam --built in, truck, SUV) but can't we try out few options from a finite list and figure out which works best? plus horizontal restrictions on vehicle width is even better. nearly all consumer passenger vehicles on the road fall within a remarkably narrow band of 1.75 m to 2.00 m in width (excluding mirrors), with an overwhelming majority hovering right around 1.80 m to 1.85 m.
07:48 · repliedBoth points are right, and the second one exposes a bug in what we measured. The vehicle-width estimator gave a median height of 1.79 m with an interquartile range of 0.94, which is far too scattered for a prior that tight. The prior isn't the problem, the measurement is: a bounding box around a car seen at three-quarter view spans its diagonal, not its width, so every angled vehicle inflates the estimate. Measuring mask width at the axle row on rear-facing vehicles only should fix it.
And there's a structural reason this approach should win. Lateral distance depends on height and horizon alone, the focal cancels. So vehicle width and lane width are two independent physical rulers that both constrain height and nothing else. Solve height from one, check it against the other. That's the non-circular test the first attempt lacked.
07:48 · repliedBimodal: 73 frames return a near-zero shift (failed correlation that still clears the gate), 54 give about 1 m per frame matching the reference. The response gate is too permissive. Testing gates.
07:48 · repliedRaising the gate makes it worse: the zero-shift results have high confidence. That's the signature of duplicated frames in the source video. Checking.
07:48 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork AA, working in /Users/liwimoi/projects/bev. **File scoping is critical**: another fork (Z) is concurrently writing README.md, CLAUDE.md, docs/results.md, docs/reports/*, reports/*, experiments/a3_vo.py, src/bev/report_docx.py and work/vo/ — DO NOT create or edit any of those. You own only: `experiments/a4_height.py`, `src/bev/height.py`, `work/a4/`, `docs/height.md`. Do not modify ~/projects/fault-lab/data or ~/projects/cv. Mac only (.venv or ~/projects/fault-lab/.venv-cv for SAM 3 masks — no new pod, no GPU needed). Licence irrelevant. Time-box ~2.5 h.
**The problem.** Camera mount height H is the weak link in metric BEV: the previous run (work/a1/scores.md) got H from a vehicle-width prior with median 1.79 m and IQR 0.94 across clips, and only 1/11 clips landed in a plausible 1.2–1.5 m mount range. Ian's dia…
07:48 · repliedI'll estimate camera height from vehicle widths measured at the axle row with an orientation filter, then cross-validate against the lane-width ruler.
07:48 · repliedRunning, scoped to files the other fork isn't touching. It will do four things:
- **Fix the measurement.** Width at the axle row from the segmentation mask, not the bounding box, with filters for rear-facing, unoccluded, untruncated, large enough. It reports a funnel so we see how many detections survive, and it reports the old bbox method side by side so the size of the fix is visible against 1.79 with an interquartile range of 0.94.
- **Hypothesis selection over your finite list.** A grid from 1.10 to 2.60 m spanning sedan, SUV, van and truck mounts, scored on how well it puts vehicle width at 1.83 m and lane width at 3.25 m at once. It reports whether the objective is sharply peaked or flat, which tells us whether the data can actually pick a class.
- **The cross-validation.** Solve height from vehicles alone, then predict lane width. Solve it from lane width alone, then predict the vehicle-width distribution. If the two rulers agree, height is settled and the only remaining unknown is the focal.
- **Ego class from the hood**, marked speculative. A van or truck shows little hood, a sedan shows a wide curved one low in the frame, so the mask shape may correlate with the selected height.
Approach 3 is still running on the pod in parallel.
07:48 · repliedNo duplicate frames, so it's a correlation failure. Checking where in the clip it fails.
07:49 · repliedNot a failure — the car stops after the collision. Frames 0 to 79 give 1.1 m per frame (16.5 m/s), frames 80 onward are genuinely stationary. Updating the summary to separate moving from stopped.
07:49 · replied053, 042 and 023 now match the reference speeds. 011 reads stationary because it has no joint fit, so the hood row defaulted and the static hood dominates. Pulling the hood row from the ego mask instead.
07:50 · repliedData is in place: SAM 3 masks at 1280×720, full 150 frames for five clips and the 45-frame window for the rest. Writing the estimator now.
07:50 · repliedCheckpoints are down and no compiler is running. Checking whether the build finished or died.
07:50 · repliedThe nvcc build failed — the known torch 2.8 incompatibility. One capped attempt at the standard patch, then I terminate.
07:51 · repliedVehicle estimator moved 053 from 0.91 m raw-bbox to 1.36 m, against 1.12 m from the lane ruler. Running all 12 with a roll sensitivity check.
07:52 · repliedRoll correction from the joint fit makes the spread worse, on 042 dramatically. That says the joint roll is noise. Better: derive roll from the data by minimising spread, which is cheap if I store raw measurements and apply roll analytically.
07:52 · repliedWhile that rebuilds, fixing the 011 hood row locally.
07:53 · repliedWhile the rebuild runs, writing the scoring for Approach 3.
07:54 · askedi guess now the importance of pose estimation (other vehicle's angle) is important here as we require the vehicle width ruler to be only relevant if it's aligned as rear or front.
07:54 · repliedClip 053 is the key result: with roll fitted from the vehicles, the two independent rulers agree to 0.7%. Writing the full experiment.
07:54 · repliedExactly right, and we may already have the pose data. The bev fork ran UrbanOmniDetect on all 12 clips, and per the survey it outputs 3D cuboid keypoints per vehicle without needing intrinsics. Let me check whether its output carries usable orientation.
07:54 · repliedUOD gives 8-corner cuboids with the four ground-contact corners per vehicle. That's better than a filter: instead of discarding angled vehicles, we can measure the *short edge* of each vehicle's ground footprint, which is its true width at any viewing angle. And the length-to-width ratio of that footprint, about 2.4 to 2.6 for a sedan, is a scale-free check that the projection is right. Sending this to the running fork.
07:55 · repliedSent. Three things follow from your observation.
**The width ruler gets stronger, not narrower.** With per-vehicle footprints we measure the short edge at any angle, so the sample grows from "rear-facing only" to every detected vehicle across all 150 frames. The long edge adds a second prior, roughly 4.4 to 4.8 m for a sedan, keyed by the detected class. And the length-to-width ratio is independent of camera height entirely, which makes it a free validity check on each measurement before it enters the average.
**Vehicle pose pays twice.** It's not only a calibration input. Heading relation is a KB question the engine asks directly, and the counterpart's lateral movement was the VLM's worst failure at 2 of 12. A per-vehicle yaw on the ground plane answers both, and it's what makes "when did the other car start moving sideways" measurable rather than described.
**One caveat on the source.** UOD's own labels are marked pseudo ground truth, model output refined offline. Its cuboid head can be wrong, especially on partial or distant vehicles, which is why the aspect-ratio filter matters and why the axle-row estimator stays in as an independent check. If the two vehicle rulers and the lane ruler all converge on the same height, that's a real answer. If UOD's footprints alone say one thing, it's a hypothesis.
07:55 · repliedChecking the UrbanOmniDetect labels before folding them in.
07:56 · repliedUrbanOmniDetect gives cuboid corners, and one detail matters: projecting them to metres needs the focal for the forward axis, so the footprint's short edge is not focal-free the way a rear-facing axle width is. But that cuts the other way — the aspect ratio is height-invariant and focal-sensitive, which makes it a focal ruler. Implementing both.
07:57 · repliedUOD adds a real signal on 042 where the focal-from-aspect is tight (±120 px) but fails on 053 where the cuboids are poor. Integrating all three estimators plus the yaw filter.
07:58 · repliedNow updating the report to carry three estimators and the focal finding, then running all 12.
07:59 · repliedPrecise, fixable error: `torch::linalg::linalg_norm` moved namespaces in torch 2.8. Three occurrences. Patching and rebuilding.
07:59 · repliedSeveral clips give implausible heights, and the pattern points at the horizon: H scales directly with the distance below it. Quantifying that sensitivity, since it decides whether the weak link is the ruler or the horizon.
08:01 · repliedNumbers are complete. Writing the verdict.
08:01 · repliedRebuild running. Writing the project files while it compiles.
08:02 · repliedThe patch worked — it's past the error and compiling. Writing project files while it builds.
08:02 · repliedEstimated camera height from three lateral rulers and tested whether they agree.
**Files** (only my owned paths): `src/bev/height.py`, `experiments/a4_height.py`, `docs/height.md`, `work/a4/{results.json, curves.json, horizon_sensitivity.json, vis/<clip>.png ×12}`. Nothing committed.
**Verdict: no on both questions.**
The axle-row measurement plus the orientation filter moves the median implied height from 1.06 m (same masks, raw bbox) to 1.48 m — the direction the diagonal-inflation diagnosis predicts — but the across-clip IQR widens 0.44 → 0.68 m and only 3/12 clips land in a plausible passenger-car band. Against the independent lane ruler: **1/5** clips agree within ±10 %, 2/5 within ±25 %. Clip 053 is the one clean case (ratio 0.99; each ruler predicts the other's quantity to 1 % — 3.23 m lane, 1.84 m vehicle).
Mount-class selection is not sharp. Because both width terms are linear in H, the cost curve is peaked only when the rulers already agree, so its width is a disagreement meter, not a resolution limit — and across twelve ordinary dashcams it picks classes from 해치백 to 승합·밴, which cannot all be right.
**Where the error lives, in order.**
1. **The horizon.** H ∝ distance below it, so the 27 px median disagreement between the three horizon routes alone swings H by **22 %** (003: 2.50 → 1.45 m). It is a *shared* multiplicative error, which is why the ruler ratio is sounder than any absolute height.
2. **Cuboid depth.** Ruler B is unusable here. At the depth models' own focal the median car footprint has a length/width ratio of **2.0–6.0** against a true 2.51 — the cuboids are far too elongated. The "focal from aspect" that fixes it comes out 540–1130 px against 1360–1880 px, so that figure is compensating for a bad cuboid, not measuring a lens. The geometry i…
08:02 · asked<task-notification>
<task-id>ac1188a5f17b821e0</task-id>
<tool-use-id>toolu_01XMiVNjB2DJJ1oGcftKE9EX</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/ac1188a5f17b821e0.output</output-file>
<status>completed</status>
<summary>Agent "Camera height by hypothesis selection" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Estimated camera height from three lateral rulers and tested whether they agree.
**Files** (only my owned paths): `src/bev/height.py`, `experiments/a4_height.py`, `docs/height.md`, `work/a4/{results.json, curves.json, horizon_sensitivity.json, vis/<clip>.png ×12}`. Nothing committed.
**Verdict: no on both questions.**
The axle-row measurement plus the orientation filter moves the median implied height from 1.06 m (same masks, raw bbox) to 1.48 m — the direction the diagonal-inflation diagnosis predicts — but the across-clip IQR widens 0.44 → 0.68 m and only 3/12 clips land in a plausible passenger-car band. Against the independent lane ruler: **1/5** clips agree within ±10 %, 2/5 within ±25 %. Clip 053 is the one clean case (ratio 0.99; each ruler predicts the other's quantity to 1 % — 3.23 m lane, 1.84 m vehicle).
Mount-class selection is not sharp. Because both width terms are linear in H, the cost curve is peaked only when the rulers already agree, so its width is a disagreement meter, not a resolution limit — and across twelve ordinary dashcams it picks classes from 해치백 to 승합·밴, which cannot all be right.
**Where the error lives, in order.**
1. **The horizon.** H ∝ distance belo…
08:03 · repliedNegative result, but it relocates the problem, and it corrects something I told you earlier.
**Your diagnosis was right about the measurement.** Fixing it moved the median height from 1.06 m with raw boxes to 1.48 m at the axle row, exactly the direction the diagonal-inflation theory predicts. But it did not converge: the spread across clips widened, and only 3 of 12 clips land in a plausible passenger-car band.
**The cross-validation says the rulers mostly disagree.**
| Vehicle ruler vs lane ruler | Clips |
|---|---|
| Agree within 10% | 1/5 |
| Agree within 25% | 2/5 |
Clip 053 is the clean case: each ruler predicts the other's quantity to within 1%, giving a 3.23 m lane and a 1.84 m vehicle. When the geometry is right it is *visibly* right, which is what makes the rest interpretable.
**The binding constraint is the horizon, not the height prior.** Height is proportional to distance below the horizon, so the 27 px of disagreement between our three horizon routes swings the height by 22%. On clip 003 that is 2.50 m versus 1.45 m from the same pixels. I called pitch and roll "solid" two days ago because three methods agreed within about 20 px. That was right for orientation and wrong for scale, and I should have said so then. Because the horizon error is shared by every ruler, the *ratio* between rulers stays meaningful even when both absolute numbers are wrong, which is why 053 can be self-consistent.
**Your finite-list idea works, but as a gate rather than a selector.** The cost curve is sharp only when the rulers already agree, so its width measures disagreement instead of resolving it. Across twelve ordinary dashcams it selects classes from hatchback to van, which cannot all be true. The useful form is: where the rulers agree, trust the height; where they don'…
08:03 · repliedWriting the engineering results doc.
08:04 · repliedSecond known torch 2.8 patch: `.type()` → `.scalar_type()` in the dispatch macros. Applying.
08:06 · repliedWriting the three Korean reports while it builds.
08:08 · repliedCommitting while the build finishes.
08:10 · repliedBuild is still compiling well past the 40-minute window the directive set for the extensions. I have a complete Approach 3 locally, so I'm terminating rather than letting the pod bill on a maybe.
08:10 · repliedPod terminated and confirmed gone.
## Report
**Approach 3 — the numbers**
*Focal from motion: fails, and for a structural reason.* Bougnoux over fundamental matrices on the static scene:
| clip | motion median (px) | p10–p90 | valid pairs | WildCamera |
|---|---|---|---|---|
| 053 | 364 | 214–751 | 24/49 | 1281 |
| 011 | 407 | 200–940 | 16/49 | 1264 |
| 042 | 541 | 231–1246 | 13/49 | 1333 |
| 023 | 205 | 172–426 | 9/49 | 1365 |
| 035 | 494 | 249–982 | 22/49 | 1207 |
Median ratio 0.32× to WildCamera, spread of 3–5× *within* a clip, **0/5** within ±25% of any single-image source. Forward translation along the optical axis is a degenerate configuration for focal-from-F, and a dashcam does exactly that. Not worth another attempt.
*Metric ego-motion: works.* Decomposing the raw road homography is ill-conditioned (the visible band is far and barely moves), so I rectified the road to a metric strip and phase-correlated — forward motion becomes a pure translation, camera height the only scale anchor.
| clip | solved | speed moving (m/s) | reference S1 | S1 dash frames | stop frame |
|---|---|---|---|---|---|
| 053 | 142/149 | 14.8 | 15.0 | 48/150 | 94 |
| 042 | 94/149 | 19.3 | 18.8 | 67/134 | 97 |
| 023 | 99/149 | 16.9 | 20.2 | 88/144 | — |
| 035 | 131/149 | 8.4 | 11.0 | 56/150 | — |
| 011 | 92/149 | 5.7 | 15.2 | **14/70** | 75 |
Agreement tracks how much the reference actually measured. **This found an error in fault-lab's world map**: S1 fills unmeasured frames with a median speed, so the ego keeps moving after it has stopped — 053 reads 161 m where the real path is 75 m, and 011 reads 15.2 m/s on a clip whose gold annotation says *stopped or waiting*. That stretches the accumulated map along the direction of travel.
Pitch/roll from the plane normal vs the vanishing …
08:10 · asked<task-notification>
<task-id>abc37f85a717a5708</task-id>
<tool-use-id>toolu_01Eg8K42zhepRpvBaBnHW5qf</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/abc37f85a717a5708.output</output-file>
<status>completed</status>
<summary>Agent "Finish bev approach 3 + reports" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Pod terminated and confirmed gone.
## Report
**Approach 3 — the numbers**
*Focal from motion: fails, and for a structural reason.* Bougnoux over fundamental matrices on the static scene:
| clip | motion median (px) | p10–p90 | valid pairs | WildCamera |
|---|---|---|---|---|
| 053 | 364 | 214–751 | 24/49 | 1281 |
| 011 | 407 | 200–940 | 16/49 | 1264 |
| 042 | 541 | 231–1246 | 13/49 | 1333 |
| 023 | 205 | 172–426 | 9/49 | 1365 |
| 035 | 494 | 249–982 | 22/49 | 1207 |
Median ratio 0.32× to WildCamera, spread of 3–5× *within* a clip, **0/5** within ±25% of any single-image source. Forward translation along the optical axis is a degenerate configuration for focal-from-F, and a dashcam does exactly that. Not worth another attempt.
*Metric ego-motion: works.* Decomposing the raw road homography is ill-conditioned (the visible band is far and barely moves), so I rectified the road to a metric strip and phase-correlated — forward motion becomes a pure translation, camera height the only scale anchor.
| clip | solved | speed moving (m/s) | reference S1 | S1 dash frames | stop frame |
|---|---|---|---|---|---|
| 053 | 142/149 | 14.8 | 15.0 | 48/150 | 94 |
| 042 | 94/149 | 19.3 | 18.8 | 67/134 | 97 |
| 0…
08:11 · repliedApproach 3 is done, pod terminated at $0.91. It produced a structural answer, a working method, and a bug in something I reported to you earlier.
08:11 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are Fork AB. Fix the ego-speed error the bev project's visual-odometry work exposed in fault-lab's world map, and rebuild the affected outputs. Work in /Users/liwimoi/projects/fault-lab; read ~/projects/bev freely; do not modify data/ in either project; do not touch ~/projects/cv. Mac only, no pod. Time-box ~2 h.
**The bug.** `src/faultlab/worldmap.py` stage-1 (S1) ego pose fills frames where dash-phase speed was not measured with the clip's *median* speed. On 053 dash phase fired on 48/150 frames, so the ego keeps rolling after it has actually stopped: S1 reports a 161 m path where the true path is ≈75 m. On 011 S1 reports 15.2 m/s from only 14/70 measured frames, on a clip whose gold annotation (`data/stage0_gold/011.json` ego_prior_state) says the ego was stopped or waiting. Accumulating paint under an over-long tra…
08:11 · repliedI'll fix the ego-speed error in fault-lab's world map using the bev project's VO speeds, then rebuild the maps, viewer, and Korean report.
08:11 · replied**Focal from motion fails for a structural reason, not a tuning one.** Estimating focal from the fundamental matrix is degenerate when the camera translates along its own optical axis, which is precisely what a dashcam does. Median estimate came out at 0.32 of the single-image value with a 3 to 5 times spread inside a single clip, 0 of 5 clips within 25% of any other source. That route is closed, and knowing why saves us from retrying it.
**Metric speed from motion works.** Rectifying the road band into a metric strip turns forward motion into a pure translation, and phase correlation reads it off. Coverage was 92 to 142 of 149 frames per clip, and the speeds match the dash-phase reference wherever that reference actually had measurements.
**And that exposed a real error in the world map I showed you.** Stage 1 filled unmeasured frames with the clip's median speed, so the ego kept driving after it had stopped. Clip 053 reported a 161 m path where the true path is about 75 m. Clip 011 reported 15.2 m/s from only 14 measured frames, on a clip whose own gold annotation says the ego was stopped or waiting. An over-long trajectory smears the accumulated paint, so the sharpness figures I gave you are wrong on at least three clips. A rebuild with the corrected speeds is running now, and sharpness is the test: a right path should make the dashes crisper, not blurrier.
**The two forks disagree about the weak link, and the disagreement is instructive.** The visual-odometry work says focal, unambiguously, and that solving height from lane width beats assuming 1.3 m, 66 of 84 against 46 of 84 on the lane-width test. The height work says the horizon, since 27 px of horizon disagreement swings height by 22%. Both are partly right, and there's a catch worth naming: the 66 versus 46…
08:12 · repliedNow I'll write the corrected speed module.
08:13 · repliedNow wire the switch through `run()` and the CLI.
08:14 · repliedNow a smoke test on 053, the clip with the largest known error.
08:15 · repliedPath corrected from 161 m to 70 m, matching the bev reference of 74.9 m. Running the other four to get the full comparison.
08:17 · repliedPath is fixed on three clips but sharpness moved the wrong way on three. Two changes are confounded: the speed source and the lighter smoothing. Isolating them.
08:18 · repliedClean isolation: `dash+savgol` reproduces the old numbers exactly, so the regression is from the speed source, not the filter. Checking whether the old pipeline was compensating a stretched path with a distorted focal.
08:19 · repliedIsolation is clean: the old path is reproduced exactly with `--speed dash`, so the regression comes from the speed source, not the filter. Now regenerating the real outputs with video.
08:21 · repliedNow the scores section, using the regenerated outputs.
08:22 · repliedNow the viewer refresh and re-assembly.
08:22 · repliedViewer validates and carries the corrected paths. Now the Korean report.
08:23 · repliedFixed the ego-speed bug in fault-lab's world map, rebuilt all five clips, viewer and Korean report.
**Old vs new** (`--speed dash` reproduces the old numbers exactly, so the speed source is the only change):
| clip | path m old→new (bev ref) | measured/interp/held | stop frame | sharpness cm | lane W m | contact m |
|---|---|---|---|---|---|---|
| 053 | 161.3→**70.2** (74.9) | 139/11/0 | 95 | 30→30 | 3.54→6.11 | 12.95→14.49 |
| 011 | 154.4→**0.5** (4.2) | 91/32/27 | none | 55→**30** | 3.77→3.41 | 6.56→7.61 |
| 042 | 186.2→**54.2** (47.9) | 93/32/25 | 99 | 40→90 | 3.24→4.45 | none |
| 023 | 204.7→205.7 (110.4) | 85/65/0 | none | 20→30 | 3.93→3.90 | none |
| 035 | 132.0→140.1 (93.5) | 113/36/1 | 149 | 50→60 | 3.33→3.55 | 3.19→3.11 |
**Path lengths are fixed on the three affected clips**, matching the independent implementation. 023 and 035 barely moved because they inherit fault-lab's focal (MoGe corrected by dash pitch) rather than bev's WildCamera focal — the rectified raster's metre scale is set by f·H, so **the VO speed inherits the camera error and is not an independent scale anchor** (same algorithm, different camera, up to 1.9× apart).
**Sharpness did not improve** — 1/5 better, 1/5 flat, 3/5 worse. I isolated the filter change (savgol→median) as a possible cause and it is not: `vo+savgol` is equally bad on 042 (70 cm). The reason is the metric itself: **sharpness measures trajectory smoothness, not correctness.** A median-filled trajectory was uniformly wrong but smooth, so a uniformly stretched map still has crisp dash edges (only the spacing is wrong). The focal correction `f_scale_total` is identical old vs new, so f was not compensating either. Near-field smear remains attributable to uncorrected per-frame pitch, as originally diagnosed.
**Stop vs labels:…
08:23 · asked<task-notification>
<task-id>a7e46afcf622874b8</task-id>
<tool-use-id>toolu_01Buq3ZSwdkTk4udhtNGveuM</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-fault-lab/6455566d-dd9e-4507-9c25-25e8dd17342a/tasks/a7e46afcf622874b8.output</output-file>
<status>completed</status>
<summary>Agent "Rebuild world map with corrected ego speed" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Fixed the ego-speed bug in fault-lab's world map, rebuilt all five clips, viewer and Korean report.
**Old vs new** (`--speed dash` reproduces the old numbers exactly, so the speed source is the only change):
| clip | path m old→new (bev ref) | measured/interp/held | stop frame | sharpness cm | lane W m | contact m |
|---|---|---|---|---|---|---|
| 053 | 161.3→**70.2** (74.9) | 139/11/0 | 95 | 30→30 | 3.54→6.11 | 12.95→14.49 |
| 011 | 154.4→**0.5** (4.2) | 91/32/27 | none | 55→**30** | 3.77→3.41 | 6.56→7.61 |
| 042 | 186.2→**54.2** (47.9) | 93/32/25 | 99 | 40→90 | 3.24→4.45 | none |
| 023 | 204.7→205.7 (110.4) | 85/65/0 | none | 20→30 | 3.93→3.90 | none |
| 035 | 132.0→140.1 (93.5) | 113/36/1 | 149 | 50→60 | 3.33→3.55 | 3.19→3.11 |
**Path lengths are fixed on the three affected clips**, matching the independent implementation. 023 and 035 barely moved because they inherit fault-lab's focal (MoGe corrected by dash pitch) rather than bev's WildCamera focal — the rectified raster's metre scale is set by f·H, so **the VO speed inherits the camera error and is not an independent scale anchor** (same algorithm, different camera, up to 1.9× apart).
**Sharpness did not improve** — 1/5 better, 1…
08:24 · repliedCorrected world map is published at the same link. The fix worked on trajectory and exposed two things I had wrong.
**Path lengths are now right, and one clip validates decisively.**
| Clip | Path, old → new | Independent reference | Stop frame |
|---|---|---|---|
| 053 | 161.3 → 70.2 m | 74.9 m | 95 |
| 011 | 154.4 → 0.5 m | 4.2 m | none needed |
| 042 | 186.2 → 54.2 m | 47.9 m | 99 |
Clip 011 is the proof: the ego now reads as essentially stationary, which is exactly what its gold annotation says. The old version had it driving 154 m at 15 m/s on a clip labelled stopped or waiting. Detected stops agree with the labels on all five clips, including 053 stopping two seconds after impact and 042 immediately after.
**My sharpness metric doesn't measure what I said it measured.** I told you a crisp map proves the poses are right. It doesn't. Sharpness only got better on 1 of 5 clips and worse on 3, and the reason is that a uniformly wrong but smooth trajectory still stacks each dash on itself neatly, just at the wrong spacing. It is a smoothness meter, not an accuracy meter. The fork ruled out the filter change as the cause, so this is the metric's own limitation.
**And the corrected speed is not an independent scale anchor.** The rectified road strip's metric scale is set by focal times height, so the visual odometry inherits whatever camera error we already had. The same algorithm run with fault-lab's focal and with the bev project's focal differs by up to 1.9 times. The two implementations agreeing on 053 and 042 partly reflects shared assumptions rather than confirmation.
**One thing got worse and it points at the next error.** Lane width measured on the map went from 3.54 to 6.11 m on 053 and 3.24 to 4.45 m on 042. With a much shorter path, the same paint piles i…