feat(pipeline): End-to-end validation with ground truth meshes

[hummat]

Summary

Add real end-to-end testing by reconstructing objects with known ground truth meshes and textures, then comparing results via quantitative metrics.

Motivation

Current tests validate CLI/script behavior but not reconstruction quality. Need regression detection for actual output quality when pipeline or models change.

Proposed Design

Trigger & Execution

• Manual kick-off (e.g., scripts/validate.sh)
• Runs overnight — full pipeline too expensive for CI

Test Assets (stored in R2)

• Location: r2:hummat-assets/mini-mesh/validation/
• Initial objects:
  • 016_mokka — geometry only (from automatica dataset)
  • 018_mustard_bottle — geometry only (from automatica dataset)
  • YCB mustard bottle — geometry + texture (for texture validation)
• Format: GT mesh (.ply/.obj), test video, metadata (expected scale, etc.)

Geometry Metrics

Metric	Purpose
Chamfer distance	Overall surface accuracy
Hausdorff distance	Worst-case error (catches outliers, thin structures)
F-score @ threshold	% of surface within tolerance (e.g., 1mm, 5mm)

Alignment Pipeline

1. Fast Global Registration (FGR) — coarse alignment using FPFH features (handles unknown correspondences)
2. Scaled ICP — refine with scale estimation (TransformationEstimationPointToPoint(with_scaling=True))
3. Fallback — if FGR fitness < threshold, use PCA-based init (centroid + principal axes) → scaled ICP
4. Outlier filtering — remove predicted points far from GT (handles unmasked runs with extra geometry like table surface)
5. Compute metrics — on filtered, aligned point sets

Texture Metrics

• Method: Render-based comparison
  • Render GT and reconstructed mesh from N viewpoints
  • Compare rendered images via PSNR, SSIM, LPIPS
• Captures: Albedo accuracy + sharpness
• Renderer: Trimesh + pyrender (default), nvdiffrast for speed

Output

• JSON report: Machine-readable metrics for tracking
• Markdown summary: Human-readable results

Regression Detection

• Initially: Manual inspection, no automatic pass/fail
• Once baselines established: Relative with absolute floor
  • Flag if metrics degrade > X% from stored baseline
  • OR if metrics fall below absolute minimum acceptable quality
  • Catches both sudden regressions and slow drift over time

Alternatives Considered

1. Manual visual inspection only: Compare reconstructed meshes by eye in Blender/MeshLab. Rejected because: subjective, not reproducible, doesn't scale, can't detect subtle regressions.

2. Synthetic data with perfect GT: Render synthetic scenes and reconstruct them. Considered but deprioritized because: doesn't test real-world capture challenges (lighting variation, motion blur, reflections), though useful as a complementary test later.

3. Unit tests on pipeline stages only: Test individual components (SfM accuracy, mesh extraction) in isolation. Rejected as primary approach because: integration bugs and quality drift across stages would go undetected. Current CLI tests already cover this partially.

4. CI integration with smaller assets: Run validation on every PR with tiny test objects. Rejected because: meaningful reconstruction takes hours on GPU; tiny objects don't exercise the full pipeline realistically. Manual/nightly trigger is more practical.

Tasks

• Set up R2 storage structure for validation assets
• Record/prepare test videos for initial objects
• Upload GT meshes and videos to R2
• Implement alignment pipeline (FGR + scaled ICP + PCA fallback + filtering)
• Implement geometry metrics (Chamfer, Hausdorff, F-score)
• Implement render-based texture comparison
• Create scripts/validate.sh entry point
• Generate JSON + Markdown reports
• Document usage in docs/
• Add baseline storage and regression detection (after initial baselines captured)

Dependencies

• trimesh — mesh loading, point sampling
• pyrender — rendering
• lpips — perceptual similarity
• open3d — FGR, scaled ICP, point cloud operations

Future Considerations

• Add more objects incrementally (challenging cases: reflective, thin structures)
• Integration with scheduled runs (nightly/weekly)

Related Issues

• feat(export): PBR-ish texture extraction and BRDF-aware training #11 — PBR texture extraction (texture validation useful for PBR outputs)

[hummat]

Related but distinct from #39. This issue scores the end of the pipeline against GT meshes, which folds SfM error, training and mesh extraction into one number. #39 scores SfM alone against known poses. The synthetic-GT alternative rejected here as option 2 turns out to exist already: MUGSQA released 1,970 samples over 55 objects with exact poses, covering view count, resolution, camera distance and initialisation quality.