nbrightandClaude Opus 5
609d9a6972
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>
56 lines
1.8 KiB
Bash
56 lines
1.8 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - Korean data proof of concept
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#
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# Scope is deliberately one tile. The point is to answer "does our data go
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# through this pipeline at all", not to measure anything. Nothing here produces
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# a number worth quoting.
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#
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# Source: Seosan Myeongcheon, 구역2(미션7)/BlockYBA
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# ContextCapture OBJ, 4,553,583 faces, 17 texture atlases
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# local metric coords, EPSG:5186+9999, SRSOrigin 155184.79/469705.03/149.57
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# block extent 465.36 x 487.46 x 27.55 m
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#
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# We cut one 252 m tile out of the middle -- 252 m is the size of the tile
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# SUM Parts ships, so the point density and neighbourhood radii line up with
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# what the cfgs assume.
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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SCRIPTS="/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/scripts"
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SRC="/mnt/d/MyProject_대용량샘플/02. 데이터/01. 서산 명천_08월/02. 본태모델/구역2(미션7)/BlockYBA/BlockYBA.obj"
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OUT_DIR="$HOME/sum-parts/data/korea_poc"
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OUT="$OUT_DIR/seosan_BlockYBA_tile0.ply"
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# centre 252 m tile of the block's 465 x 487 m footprint
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X0=300; Y0=720; X1=552; Y1=972
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# demo_texsp_pcl.ply carries 471,726 points over a 252 m tile; match it so the
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# voxel_size 0.02 / voxel_max 64000 settings behave the same way
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POINTS=470000
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate "$ENV_NAME"
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if [ ! -f "$SRC" ]; then
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echo "error: source mesh not found:" >&2
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echo " $SRC" >&2
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exit 1
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fi
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mkdir -p "$OUT_DIR"
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echo "=== converting one 252m tile ==="
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python "$SCRIPTS/mesh_to_ply.py" "$SRC" "$OUT" \
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--points "$POINTS" \
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--bbox "$X0" "$Y0" "$X1" "$Y1" \
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--label 0
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echo
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echo "=== verifying against the SUM Parts schema ==="
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python "$SCRIPTS/check_ply.py" "$OUT" \
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"$HOME/sum-parts/data/pcl/face_labeling_pcl/demo_texsp_pcl.ply"
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echo "POC CONVERT DONE"
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