zed-playground/py_workspace/.sisyphus/notepads/full-icp-pipeline/learnings.md

• Corrected success gate logic to > 0.
• Added INFO logging for all attempted ICP pairs.
• Ensured all pairs are stored in metrics.per_pair_results.
• Fixed overlap skip logging to use DEBUG level.
• Fixed syntax and indentation errors in aruco/icp_registration.py that were causing unreachable code and malformed control flow.
• Relaxed success gate to metrics.num_cameras_optimized > 0, allowing single-camera optimizations to be considered successful.
• Implemented comprehensive per-pair diagnostic logging: INFO for ICP results (fitness, RMSE, convergence) and DEBUG for overlap skips.
• Ensured all attempted ICP results are stored in metrics.per_pair_results for better downstream diagnostics.
• Updated tests/test_icp_registration.py to reflect the new success gate logic.

Task 3: 3D AABB Overlap Check

• Implemented compute_overlap_3d in aruco/icp_registration.py.
• Added overlap_mode to ICPConfig (defaulting to "xz").
• Verified 3D overlap logic with new tests in tests/test_icp_registration.py.
• Confirmed that empty inputs return 0.0 volume.
• Confirmed that disjoint boxes return 0.0 volume.
• Confirmed that partial and full overlaps return correct hand-calculable volumes.

Task 2: Point Extraction & Preprocessing

• Implemented extract_scene_points with floor, hybrid, and full modes.
• Implemented preprocess_point_cloud with statistical outlier removal (SOR).
• Added region field to ICPConfig dataclass.
• Added comprehensive tests for new extraction modes and preprocessing.
• Verified backward compatibility for floor mode.
• Verified hybrid mode behavior (vertical structure inclusion and fallback).
• Verified full mode behavior.
• Verified SOR preprocessing effectiveness.

Task 4: TukeyLoss Robust Kernel Support

• Added robust_kernel and robust_kernel_k to ICPConfig.
• Implemented TukeyLoss application in pairwise_icp for both Point-to-Plane and Generalized ICP.
• Verified that TukeyLoss correctly handles outliers in synthetic tests, maintaining convergence accuracy.
• Default behavior remains backward-compatible with robust_kernel="none".

Task 6: FPFH+RANSAC Global Pre-alignment

• Implemented compute_fpfh_features and global_registration using Open3D RANSAC.
• Added global_init flag to ICPConfig (default False).
• Integrated global registration into refine_with_icp as a pre-alignment step before pairwise ICP.
• Added safety checks: global registration result is only used if fitness > 0.1 and the resulting transform is within max_rotation_deg and max_translation_m bounds relative to the initial extrinsic guess.
• Verified with synthetic tests:
  • test_compute_fpfh_features: Validates feature dimension and count.
  • test_global_registration_known_transform: Confirms RANSAC can recover a known large transform (30 deg rotation).
  • test_refine_with_icp_global_init_success: End-to-end test showing global init can recover from a very bad initial guess (90 deg error) where local ICP would fail.

Task 8: Relax ICPConfig defaults

• Relaxed defaults for ICPConfig to improve convergence and allow more flexible corrections.
• New defaults:
  • min_fitness: 0.15
  • min_overlap_area: 0.5
  • gravity_penalty_weight: 2.0
  • max_correspondence_distance_factor: 2.5
  • max_translation_m: 0.3
  • max_rotation_deg: 10.0
• Verified with 36 passing tests and clean basedpyright (0 errors, though many warnings due to missing stubs).