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/setup_pointnext.sh

@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+# SUM Parts - PointNeXt_bundle dependencies + CUDA extension build
+#
+# The upstream install.sh assumes python 3.7 / torch 1.12.1 / cu113 and pins
+# requirements.txt to versions that no longer resolve on python 3.10.
+# This script keeps the same package set but relaxes the pins, and drops
+# packages that are not needed for the sumv2 segmentation task:
+#   - deepspeed   (not used by the sumv2 training path, heavy build)
+#   - mkdocs-*    (docs only)
+# It also adds two packages the code needs but requirements.txt omits:
+#   - plyfile : imported by both sumv2 dataset loaders
+#   - wandb   : main.py imports it at module scope, so it must exist even when
+#               wandb.use_wandb=False. Run with WANDB_MODE=disabled.
+# NOTE: the chamfer_dist / emd extensions look reconstruction-only, but
+# openpoints/models/__init__.py imports .reconstruction unconditionally, so
+# segmentation runs need them too. build_ext.sh builds them.
+set -euo pipefail
+
+CONDA_ROOT="$HOME/miniconda3"
+ENV_NAME="sumparts"
+REPO="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle"
+
+source "$CONDA_ROOT/etc/profile.d/conda.sh"
+conda activate "$ENV_NAME"
+
+# RTX 3060 == compute capability 8.6. Pin it so nvcc does not build every arch.
+export TORCH_CUDA_ARCH_LIST="8.6"
+export CUDA_HOME="$CONDA_PREFIX"
+export PATH="$CUDA_HOME/bin:$PATH"
+
+echo "=== [1/4] python deps ==="
+# setuptools 69.5.1 : subsampling/setup.py imports numpy.distutils, which needs
+# distutils.msvccompiler -- removed in setuptools 74.0. The extensions also
+# still use `python setup.py install`, dropped in setuptools 80.
+pip install --no-cache-dir "setuptools==69.5.1" wheel
+
+pip install --no-cache-dir \
+    plyfile \
+    scikit-learn \
+    ninja \
+    easydict \
+    PyYAML \
+    tensorboard \
+    termcolor \
+    tqdm \
+    multimethod \
+    h5py \
+    matplotlib \
+    pandas \
+    shortuuid \
+    gdown \
+    Cython \
+    pyvista \
+    wandb \
+    trimesh \
+    pillow \
+    "numpy<2"
+
+echo "=== [2/4] torch-scatter (matched to torch 2.0.1+cu118) ==="
+pip install --no-cache-dir torch-scatter \
+    -f https://data.pyg.org/whl/torch-2.0.1+cu118.html
+
+echo "=== [3/4] build CUDA extensions ==="
+# Delegated to build_ext.sh so a failed compile can be retried on its own.
+# That script also pins ninja==1.11.1.1 (torch 2.0 + ninja>=1.12 dies on SIGPIPE).
+bash "$(dirname "$0")/build_ext.sh"
+
+echo "POINTNEXT DONE"