research: align paper outline with evaluation tools

PAPER_OUTLINE.md

@@ -21,6 +21,10 @@ generated geometry, uncertainty, and lighting evidence separate?
 - Uncertainty, projection-coverage, and reprojection diagnostics.
 - Depth-normal photometric light hypothesis and shareable reveal artifacts.
 - Manifest claims contract, artifact hashes, and run summarization.
+- Reproducible depth evaluation for paired arrays and JSONL datasets, with raw
+  versus median-scaled reporting.
+- Reproducible point-cloud/Gaussian evaluation using symmetric Chamfer,
+  precision, recall, and F-score, plus calibrated-capture manifest validation.
 
 ## Preliminary reproducibility evidence
 
@@ -68,6 +72,12 @@ points labeled observed that are supported by an input view. Report failure
 cases separately for reflective, textureless, transparent, and highly
 occluded objects. Do not call relative-depth outputs metric reconstruction.
 
+Before scoring, validate the capture manifest with
+`validate_capture_manifest.py`; then use `evaluate_depth_dataset.py` and
+`evaluate_pointcloud_dataset.py` to retain per-sample failures and aggregate
+metrics. The current synthetic geometry benchmark is a contract test for
+projection/fusion determinism, not a learned-model quality result.
+
 ## Honest success criterion
 
 The method is successful only if held-out-view metrics and provenance-aware