Handoff — ReCap LAS from Abschnitt 1 (the RGB dataset)

Summary

Continuation target

Produce segment_NNN_seg3d.las files for a handful of Abschnitt 1 segments and place them on the N: share next to the existing Abschnitt 4_5 batch, so the colleague can open them in Autodesk ReCap with real colour.

The exporter itself needs no code changes for this. This is a data-selection and pipeline-invocation job, plus one genuine decision about the line/class source.

Current state

• Branch worktree-recap-las-export @ 752285d, pushed. Bitbucket PR #2 (ioholding/3dai.iolabs.pointcloud.3dsegmentation). 95 tests passing.
• Local worktree: /home/miro/dev/3dai.iolabs.pointcloud.3dsegmentation/.claude/worktrees/recap-las-export
• BattleBox scratch checkout: /mnt/d/temp/seg3d-recap-test (detached git worktree @ 23d3528; the two commits since then are docs-only, so the code there is current — but git fetch && git checkout 752285d is cheap insurance).
• Abschnitt 4_5 output: /mnt/d/temp/seg3d-recap-out/segment_{066,105,132}/, delivered to N:\…\03_Working folder\260803_recap_annotation_Abschnitt_4_5\pre_annotation\.
• Full workflow documentation: docs/recap_annotation.md in the repo. Read it before touching anything — it covers the geoshift/CRS rules and the round-trip.

Data findings verified on disk 2026-08-03

All paths on BattleBox, under /mnt/d/Data/02_AI 3D modeling/00_data/. There are three Abschnitt 1 dataset dirs and they are not interchangeable.

Why _short/lane_points is the recommended target

• Real colour, confirmed. segment_024/25-9166A1Record002_run3_points.npz: red/green/blue uint16, full 0–65535 range on all three channels, chroma mean 7995.7. Compare Abschnitt 4_5, chroma 0.0.
• 3-digit segment names. cli.parse_segments() pads to three digits (f"{int(token):03d}"), so --segments 024 resolves to segment_024. This works only here.
• Has run3_geoshift.json → {x: 690525.8662355578, y: 5384028.147794825, z: 458.4054785856573}. The exporter will add it back and stamp EPSG:25832, same as Abschnitt 4_5.
• Fattest segments: segment_024 (14 records), 017 (13), 014 / 015 / 018 / 019 / 022 / 023 (12 each). One record alone is ~3.0M points, so pick 2–3 segments, not ten — Abschnitt 4_5 segment_132 was already a 93 MB LAS.

The 2-digit blocker on _long

_long has 102 populated segments and XMLs, which makes it tempting. But its dirs are segment_00 … segment_100, so --segments 020 looks for segment_020 and finds nothing (the CLI logs dir not found … skip and returns rc=1). Options if you need _long: pass three-digit-safe numbers only (100+), create symlinks segment_0NN → segment_NN, or teach parse_segments/the seg-dir lookup to try both paddings. Do not "fix" this by renaming the source dirs — other pipelines read them.

What classes you can actually get read this

Detector mask dirs are flat segment_NNN/ with no dataset qualifier, and they contain Abschnitt 4_5 only. Verified by record name:

guardrails/out_masks/segment_066/point_masks.npz
  record_names: ['25-9166-A4-5Record002_run3_points.npz', ...]

Abschnitt 1 records:  25-9166A1Record002_run3_points.npz

Zero overlap, so masks match nothing. The stages skip with a WARNING and are recorded in stats.json → skips — they do not silently corrupt. Consequences:

• guardrail / wall / sign / gate / delineator / tree — unavailable (detectors never run on Abschnitt 1).
• ground — unavailable (tablecloth/out/seg3d_demo is 4_5 only).
• asphalt — unavailable. The in-segment *_run4_road_surface.npz is present in Abschnitt 1 segment dirs (io_npz.py:139 globs it from the segment dir), but fuse.py:402 also needs an edges file via --edges-dirs, and asphaltedge/output/run4 has no Abschnitt 1 coverage → skips with "asphalt: no edges npz found", exactly like segments 105/132 did.
• solid_line / dashed_line — only from a run7 XML. There is no clusters_mask/ anywhere in Abschnitt 1, so the cluster path is out; it must be --lines-source xml. And _short/lane_points contains no XML.

So without further work the deliverable is RGB + intensity + geometry, all points classification 1.

Next steps

1. Ask Miro open question 1 below (colour-only vs. lines). It changes step 3 and costs him one sentence. Everything else can proceed meanwhile.
2. Refresh the BattleBox checkout.

   ssh battlebox "bash -s" <<'EOF'
   export PATH="$HOME/.local/bin:$PATH"
   set -a; . ~/.env; set +a          # nexus creds — uv sync fails without these
   cd /mnt/d/temp/seg3d-recap-test
   git fetch origin && git checkout 752285d
   uv sync
   EOF

3. Run the fusion on 2–3 _short segments. Colour-only variant (no XML, no mask dirs — nothing to point them at anyway):

   D="/mnt/d/Data/02_AI 3D modeling/00_data/251017_Color_Abschnitt_1_short"
   uv run seg3d-fuse \
     --dataset-root "$D" \
     --segments 024,017,014 \
     --out /mnt/d/temp/seg3d-a1-out \
     --log-level INFO

   Leave las_rgb_mode at its default sensor. Never pass --set las_rgb_mode=class here — it overwrites RGB with the class palette and destroys the only thing this dataset is being exported for.
4. Verify before delivering. Confirm LAS 1.4 / PDRF 7, the EPSG:25832 WKT VLR, easting ≈ 6.9e5 / northing ≈ 5.38e6 (proves the geoshift was applied — note this is a different anchor from Abschnitt 4_5's 7.26e5/5.42e6), and critically chroma > 0:

   import numpy as np, laspy
   las = laspy.read("…/segment_024_seg3d.las")
   r,g,b = (np.asarray(las[c]).astype(int) for c in ("red","green","blue"))
   s = np.stack([r,g,b],1)
   print("chroma mean", (s.max(1)-s.min(1)).mean())   # must be >> 0

   A chroma of 0 means you reproduced the Abschnitt 4_5 problem and the run was pointless.
5. Deliver to N: new folder <YYMMDD>_recap_annotation_Abschnitt_1\ inside the same working folder on the N: share that the Abschnitt 4_5 batch went to (exact path is in docs/recap_annotation.md), with pre_annotation\, annotated\, reports\. Copy segment_NNN_seg3d.las + segment_NNN_stats.json + run_summary.json, and write a README.txt (copy the one in the 4_5 batch and edit — the class legend section will be much shorter, and it must say the cloud is unlabelled if that is the case).
6. Commit under AI3D-351, commit often. Never push to main, never force-push.

How to copy to N:

N: is not visible from WSL (no /mnt/n) — it is a Windows-side subst/Drive mount. Don't fight bash→cmd→powershell quoting; scp a .ps1 and run it:

scp deliver.ps1 battlebox:
ssh battlebox "powershell -NoProfile -ExecutionPolicy Bypass -File C:\Users\ai\deliver.ps1"

Inline set VAR=… && …%VAR%… in cmd does not expand and will create a literal %VAR% directory — that mistake was made and cleaned up during the 4_5 delivery. Verify byte sizes on N: afterwards; Google Drive mirrors asynchronously.

Risks and open questions

Open question 1 — colour-only, or colour + lines? ask Miro

Abschnitt 1 can give a gorgeous coloured cloud with zero pre-labels. If Miro wants pre-labelled lane markings on it, someone has to supply a run7 XML in the same frame as _short/lane_points. The XMLs that exist belong to _long and to _short/lane_points_20260506, and see risk 2. Ask which he wants before burning a run.

Risk 2 — mixing frames would silently misplace lines high

The two _short lane_points dirs have different geoshifts:

lane_points/          x 690525.8662  y 5384028.1478  z 458.4055
lane_points_20260506/ x 690523.8139  y 5384013.2839  z 458.4844

≈15 m apart in Y. Since --xml is an independent path argument, nothing stops you passing the lane_points_20260506 XML against lane_points points — and the lines would land ~15 m off the road with no error. If you use an XML, use one from the same dir as the points, or verify the geoshift matches first.

Risk 3 — segment numbers collide across datasets medium

segment_024 exists in several datasets and means a different place in each. The mask dirs are dataset-unqualified. Points and masks are joined by record name so a mismatch degrades to a skip rather than corruption — but never assume a segment_NNN path is unambiguous, and always check stats.json → skips and surface_match_rate after a run.

Risk 4 — file size

~3.0M points per record × 12–14 records per segment. Abschnitt 4_5's segment_132 (2.58M decimated points) was a 93 MB LAS. Start with one segment, check the size and ReCap-side ergonomics, then decide. --voxel raises the decimation if needed.

Not a risk — RGB bit depth is handled

writer._rgb_upscale() applies the ×257 upscale only when the whole cloud's RGB max ≤ 255, decided once per cloud. Abschnitt 1's uint16 colour (max 65535) passes through untouched; no overflow, no code change needed. This was checked, not assumed.

Key artifacts

Suggested skills

• battlebox-ssh — connection, quoting rules, drive-letter map. The data is all there.
• bitbucket-pr — file PRs directly, no drafts.
• html-plan / html-handoff — if the scope grows enough to need its own plan.

Environment gotchas

• uv is not on the default BattleBox PATH → export PATH="$HOME/.local/bin:$PATH".
• uv sync fails with "Missing credentials for nexus.iolabs.ch" unless set -a; . ~/.env; set +a runs first. It reads like a network error; it isn't.
• Long jobs die if no ssh session is attached — hold the session open (background it locally) rather than detaching remotely.
• Shared git stash across worktrees — never bare git stash/pop; use a WIP commit or a tagged git stash push -u -m.