Record results so far and fix the memory blowup in the palette decode

Adds STATUS.md as the handoff document: benchmark numbers, the bare-earth
metrics that actually matter for this project, the Korean-data domain gap that
retraining will not fix, and what to do on the 24 GB machine.

The memory problem was in how a prediction's colours were turned back into
class indices. Every consumer built an (N, 13, 3) float64 temporary:

    d = ((rgb[:, None, :] - COLOR_MAP[None, :, :]) ** 2).sum(axis=2)

That is ~250 MB of intermediates per 800k-point tile, several live at once, and
a full 4.7M-point block pushes it into gigabytes. main.py writes exact palette
entries, so an exact hash lookup resolves nearly every point with no large
temporary; only leftovers fall back to a chunked distance search. Peak RSS on a
470k-point tile drops to 61 MB. Extracted to sumparts_palette.py and shared by
coarse_eval.py and split_by_class.py.

Also from this round:

- patch_cm_mutation.sh: ConfusionMatrix.update() rewrote the caller's pred
  tensor in place, folding every ignore_index point into class num_classes-1.
  test() saves its visualization from that same tensor afterwards, so an
  unlabelled tile came out 100% wall and the model looked degenerate when it
  was not.
- patch_class_mask.sh: SUMPARTS_MASK_CLASSES drops known-absent classes from
  the argmax. Measured on Seosan and it does not help - the runner-up for
  "water" is "wall", not "terrain" - but the experiment is worth keeping.
- split_by_class.py now writes .ply alongside .obj. A vertex-only OBJ has zero
  faces and most viewers render nothing, which is why the first export looked
  broken.
- verify_outputs.sh reads exported files back with a parser, so "here are your
  files" can be checked rather than asserted.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

scripts/eval_coarse.sh

@@ -20,9 +20,10 @@ export PYTORCH_CUDA_ALLOC_CONF="garbage_collection_threshold:0.7,max_split_size_
 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")"
+# The cfg has to match the architecture that wrote the checkpoint. Hardcoding
+# pointnet.yaml here meant a PointVector checkpoint loaded into the wrong model:
+#     RuntimeError: Error(s) in loading state_dict for BaseSeg
+source "$SCRIPTS/resolve_ckpt.sh"
 
 # test() writes prediction plys and slides over whole tiles; point it at val so
 # there is ground truth to score against.
@@ -40,7 +41,7 @@ VM_ARG=()
 echo "=== inference over val split (sliding window) ==="
 echo "    test voxel_max: ${TEST_VOXEL_MAX:-null (cfg default)}"
 python -u main.py \
-    --cfg ../../cfgs/sumv2_triangle/pointnet.yaml \
+    --cfg "../../cfgs/sumv2_triangle/${CKPT_CFG}.yaml" \
     mode=test \
     --pretrained_path "$CKPT" \
     dataset.common.data_root="$DATA" \