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/smoke_train.sh

@@ -0,0 +1,74 @@
+#!/usr/bin/env bash
+# SUM Parts - pipeline smoke test
+#
+# Goal is NOT accuracy. Goal is proving the whole chain survives:
+#   ply load -> grid subsample -> cuda ops -> forward -> backward -> val
+# Uses the lightest model (pointnet) and 1 epoch. Expect garbage mIoU.
+#
+# Usage:
+#   bash smoke_train.sh                      # pointnet, 1 epoch
+#   bash smoke_train.sh pointnext-xl 2       # other cfg / epoch count
+set -euo pipefail
+
+CONDA_ROOT="$HOME/miniconda3"
+ENV_NAME="sumparts"
+REPO="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle"
+
+CFG="${1:-pointnet}"
+EPOCHS="${2:-1}"
+
+source "$CONDA_ROOT/etc/profile.d/conda.sh"
+conda activate "$ENV_NAME"
+
+export CUDA_HOME="$CONDA_PREFIX"
+export PATH="$CUDA_HOME/bin:$PATH"
+
+# main.py imports wandb at module scope, so the package must exist even with
+# wandb.use_wandb=False. Disabled mode keeps it from asking for a login.
+export WANDB_MODE=disabled
+export WANDB_SILENT=true
+
+cd "$REPO/examples/segmentation"
+
+# Two constraints squeeze BS from both sides, and both fail confusingly:
+#
+#   too large -> the loader drops the last partial batch, so a single tile with
+#     the cfg's batch_size (6 for pointnet) yields ZERO batches. The confusion
+#     matrix is then never populated and cm.all_metrics() dies on
+#         AttributeError: 'int' object has no attribute 'diag'
+#
+#   too small -> BatchNorm1d refuses a batch of 1 while training:
+#         ValueError: Expected more than 1 value per channel when training,
+#                     got input size torch.Size([1, 512])
+#
+# So: BS=2 (the BatchNorm floor), and LOOP repeats the tile enough times to fill
+# whole batches.
+LOOP="${3:-4}"
+BS="${BS:-2}"
+
+LOG="${LOG:-/tmp/sumparts_smoke.log}"
+
+echo "=== smoke train: cfg=$CFG epochs=$EPOCHS loop=$LOOP bs=$BS ==="
+echo "log: $LOG"
+
+# Capture both streams to a file. Tracebacks were getting lost when this ran
+# under a wrapper that only kept stdout, which made crashes look silent.
+set +e
+python -u main.py \
+    --cfg "../../cfgs/sumv2_triangle/${CFG}.yaml" \
+    mode=train \
+    epochs="$EPOCHS" \
+    wandb.use_wandb=False \
+    dataset.train.loop="$LOOP" \
+    batch_size="$BS" \
+    val_batch_size=1 \
+    val_freq=1 \
+    > "$LOG" 2>&1
+rc=$?
+set -e
+
+echo "=== exit=$rc | last 30 lines ==="
+tail -30 "$LOG"
+
+[ "$rc" -eq 0 ] && echo "SMOKE DONE" || echo "SMOKE FAILED (rc=$rc)"
+exit "$rc"