docs: add point cloud evaluation

EVALUATION_PROTOCOL.md

@@ -14,6 +14,7 @@ not results on a named reconstruction benchmark.
 | Depth is quantitatively accurate | Named dataset split, metric definitions, baseline, confidence interval | Not yet measured |
 | Depth evaluation is reproducible | Paired `.npy` prediction/ground-truth arrays, mask, orientation flag, standard metrics | Implemented in `evaluate_depth.py`; dataset run still pending |
 | Gaussian export is direct from a model | Native source, candidate/export counts, portable file inspection | DA3 direct path verified |
+| Exported geometry is quantitatively close | Reference point cloud, declared threshold, symmetric Chamfer and F-score | Implemented in `evaluate_pointcloud.py`; reference run pending |
 | Gaussian reconstruction is higher quality | Held-out views, renderer, PSNR/SSIM/LPIPS or equivalent, baseline | Not yet claimed |
 | Hidden-side geometry is correct | Held-out unseen-side views and a predeclared threshold | Not yet claimed |
 
@@ -77,6 +78,12 @@ run `python evaluate_depth.py prediction.npy ground_truth.npy`. Use
 `median_scaled` results; only the former is appropriate when metric scale is
 known.
 
+For point-cloud or Gaussian position evaluation, use
+`python evaluate_pointcloud.py exported.ply reference.ply --threshold 0.05`.
+The evaluator reports both directions of nearest-neighbour error, precision,
+recall, and F-score. A reference capture and predeclared coordinate alignment
+are required before interpreting these numbers as reconstruction quality.
+
 ## Current honest conclusion
 
 Simam3D demonstrates a reproducible depth/fusion/export workbench, native DA3