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/parse_ious.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# SUM Parts - map the per-class IoU array onto class names
+#
+# The log prints a bare numpy array. Guessing whether it starts at class 0 or
+# class 1 by eye is how you end up reporting "car IoU 95.6". Count it.
+set -uo pipefail
+
+LOG="${1:-$HOME/sum-parts/runs/val_ab/val_capped.log}"
+
+source "$HOME/miniconda3/etc/profile.d/conda.sh"
+conda activate sumparts
+
+python - "$LOG" <<'PY'
+import re
+import sys
+from pathlib import Path
+
+CLASSES = ['unclassified', 'terrain', 'high_vegetation', 'facade_surface',
+           'water', 'car', 'boat', 'roof_surface', 'chimney', 'dormer',
+           'balcony', 'roof_installation', 'wall']
+
+text = Path(sys.argv[1]).read_text(encoding="utf-8", errors="replace")
+
+m = re.findall(r"iou per cls is:\s*\[([^\]]*)\]", text)
+if not m:
+    print("no 'iou per cls' line found in", sys.argv[1])
+    raise SystemExit(1)
+
+vals = [float(x) for x in m[-1].split()]
+print(f"file   : {Path(sys.argv[1]).name}")
+print(f"entries: {len(vals)}  (num_classes = {len(CLASSES)})")
+print()
+
+if len(vals) == len(CLASSES):
+    names = CLASSES
+    note = "array covers classes 0..12"
+elif len(vals) == len(CLASSES) - 1:
+    names = CLASSES[:-1]
+    note = ("array is one short of num_classes. ConfusionMatrix remaps the "
+            "ignore_index into slot num_classes-1, so the last class shares a "
+            "bucket with ignored points and is dropped from the report.")
+else:
+    names = [f"class_{i}" for i in range(len(vals))]
+    note = "unexpected length -- names are positional only"
+
+print(f"note   : {note}\n")
+print(f"  {'#':>2}  {'class':<20} {'IoU':>7}")
+for i, (n, v) in enumerate(zip(names, vals)):
+    mark = "  <-- 0" if v == 0 else ""
+    print(f"  {i:>2}  {n:<20} {v:>7.2f}{mark}")
+
+nz = [v for v in vals if v > 0]
+print()
+print(f"  non-zero classes : {len(nz)}/{len(vals)}")
+print(f"  mean over all    : {sum(vals)/len(vals):.2f}")
+PY