Six papers converted to Markdown with the local doc2md tool, figures extracted
and annotated. The tool's venv had a CPU-only torch, so marker-pdf silently ran
on CPU and stalled; swapping in 2.5.1+cu121 dropped a paper from "hung after six
minutes" to three. Gemini then described all 86 figures in place, below each
original caption.
The PDFs themselves are gitignored - 88 MB of public arXiv downloads that
convert_papers.sh regenerates. The .md and figures are tracked, because the
annotations took a separate pass and do not reproduce byte-for-byte.
sum-parts-explained.html gains two tabs:
- PointVector. Why representing a scalar feature as a rotated 3D vector buys
anisotropic aggregation without attention's cost, and why the paper predicts
two independent angles rather than a rotation matrix whose nine elements are
interdependent.
- Bare Earth. Reframes the task as ground vs not-ground, and separates the five
boundaries by their nature. Four of them are cuts; the slope boundary is the
one that must NOT be cut, which is why "horizontal means ground" destroys road
cut and fill. Notes that SUM Parts is flat Helsinki and cannot teach slopes at
all, so that part needs a geometric filter rather than more training.
NEXT.md carries the goal forward: separate bare earth from the rest as OBJ
meshes, then reclassify the remainder. Removing the ground first is sound -
it is 24-40% of the points, and without it the remaining objects fall apart
into separate connected components instead of being joined through the floor.
The gap that blocks step 4 is named: mesh_to_ply.py samples points without
recording which face each came from, so there is no way back to the mesh yet.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Reproduces the SUM Parts (CVPR 2025) face-labeling benchmark on a single
consumer GPU, then applies it to drone-photogrammetry road survey meshes.
Verified on RTX 3060 12GB / WSL2 Ubuntu 22.04 / CUDA 11.8 / torch 2.0.1:
- CUDA extensions build (pointnet2_batch, pointops, chamfer_dist, emd,
subsampling)
- PointNet 100 epochs reaches mIoU 17.19, matching the paper's reported 15.1
- OBJ -> PLY conversion round-trips through the model and yields per-point
predictions
Four upstream source patches, all idempotent, originals preserved:
- numpy aliases removed in 1.24 (np.long etc.) and collections ABCs moved in
python 3.10
- the blind test split ships label = -1, which crashed ConfusionMatrix
- mode=val referenced `epoch` before assignment
Documents the traps that cost the most time, including VRAM overflow silently
falling back to host RAM on WSL2 (25-100x slowdown, no OOM) and the colour
scale mismatch between r/g/b float32 and red/green/blue uint8.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
