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zed-playground/py_workspace/.sisyphus/notepads/icp-registration/learnings.md
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## Notes
- Open3D `registration_generalized_icp` is more robust for noisy depth data but requires normal estimation.
- Multi-scale ICP significantly improves convergence range by starting with large voxels (4x base).
- Information matrix from `get_information_matrix_from_point_clouds` is essential for weighting edges in the pose graph.
- Initial relative transform from extrinsics is crucial for ICP convergence when cameras are far apart in camera frame.
- Pose graph optimization should only include the connected component reachable from the reference camera to avoid singular systems.
- Transforming point clouds to camera frame before pairwise ICP allows using the initial extrinsic-derived relative transform as a meaningful starting guess.
- Pose graph construction must strictly filter for the connected component reachable from the reference camera to ensure a well-constrained optimization problem.
- Aligned build_pose_graph signature with plan (returns PoseGraph only).
- Implemented disconnected camera logging within build_pose_graph.
- Re-derived optimized_serials in refine_with_icp to maintain node-to-serial mapping consistency.
- Open3D `PoseGraphEdge(source, target, T)` expects $T$ to be $T_{target\_source}$.
- When monkeypatching for tests, ensure all internal calls are accounted for, especially when production code has bugs that need to be worked around or highlighted.
- Integrated ICP refinement into `refine_ground_plane.py` CLI, enabling optional global registration after ground plane alignment.
- Added `_meta.icp_refined` block to output JSON to track ICP configuration and success metrics.
## ICP Registration
- GICP method in requires normals, which are estimated internally if not provided.
- Synthetic tests for ICP should use deterministic seeds for point cloud generation to ensure stability.
## ICP Registration
- GICP method in `pairwise_icp` requires normals, which are estimated internally if not provided.
- Synthetic tests for ICP should use deterministic seeds for point cloud generation to ensure stability.