Add SUM Parts reproduction and Seosan Myeongcheon application pipeline

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>

scripts/eval_coarse.sh

@@ -0,0 +1,70 @@
+#!/usr/bin/env bash
+# SUM Parts - score the trained model on the classes this project actually needs
+#
+# Runs the sliding-window test() path over the VAL split (which has real labels,
+# unlike the blind test split), then re-scores the predictions after merging
+# SUM's 13 fine classes down to building / vegetation / vehicle / ground.
+set -uo pipefail
+
+CONDA_ROOT="$HOME/miniconda3"
+SEG="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle/examples/segmentation"
+DATA="$HOME/sum-parts/data/face_labeling/texsp_pcl"
+SCRIPTS="/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/scripts"
+OUT="$HOME/sum-parts/runs/coarse_eval"
+
+source "$CONDA_ROOT/etc/profile.d/conda.sh"
+conda activate sumparts
+export WANDB_MODE=disabled WANDB_SILENT=true CUDA_HOME="$CONDA_PREFIX"
+export PYTORCH_CUDA_ALLOC_CONF="garbage_collection_threshold:0.7,max_split_size_mb:128"
+
+mkdir -p "$OUT"
+cd "$SEG"
+
+CKPT=$(find "$SEG/log/sumv2_triangle" -name '*_ckpt_best.pth' -printf '%T@ %p\n' \
+       | sort -rn | head -1 | cut -d' ' -f2-)
+echo "checkpoint: $(basename "$CKPT")"
+
+# test() writes prediction plys and slides over whole tiles; point it at val so
+# there is ground truth to score against.
+#
+# TEST_VOXEL_MAX matters more than it looks. PointNet max-pools ONE global
+# feature over whatever it is handed, so the number of points per forward pass
+# is part of the model's operating point, not a neutral batching knob. Training
+# used 64000-point crops; the cfg's test default (null) feeds ~350k-point chunks
+# and the predictions collapse toward the majority class. Set it to 64000 to
+# make inference match training.
+TEST_VOXEL_MAX="${TEST_VOXEL_MAX:-}"
+VM_ARG=()
+[ -n "$TEST_VOXEL_MAX" ] && VM_ARG=(dataset.test.voxel_max="$TEST_VOXEL_MAX")
+
+echo "=== inference over val split (sliding window) ==="
+echo "    test voxel_max: ${TEST_VOXEL_MAX:-null (cfg default)}"
+python -u main.py \
+    --cfg ../../cfgs/sumv2_triangle/pointnet.yaml \
+    mode=test \
+    --pretrained_path "$CKPT" \
+    dataset.common.data_root="$DATA" \
+    dataset.test.split=val \
+    "${VM_ARG[@]}" \
+    wandb.use_wandb=False \
+    > "$OUT/infer.log" 2>&1
+rc=$?
+if [ $rc -ne 0 ]; then
+    echo "  FAILED rc=$rc"
+    tail -15 "$OUT/infer.log"
+    exit $rc
+fi
+grep -aE 'test_oa|iou per cls' "$OUT/infer.log" | tail -4
+
+VIS=$(dirname "$(dirname "$CKPT")")/visualization
+echo
+echo "=== predictions in $VIS ==="
+ls -1 "$VIS" | grep -c '_pred.ply' || true
+
+echo
+echo "=== re-scoring at project granularity ==="
+python "$SCRIPTS/coarse_eval.py" --pred-dir "$VIS" --gt-dir "$DATA/val" \
+    | tee "$OUT/coarse.txt"
+
+echo
+echo "logs in $OUT"