feat: ignore boulder.json and update extrinsics visualizer
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{
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"active_plan": "/workspaces/zed-playground/py_workspace/.sisyphus/plans/depth-refinement-robust.md",
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"started_at": "2026-02-07T04:51:46.370Z",
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"session_ids": [
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"ses_3c99b5043ffeFGeuraVIodT6wM",
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"ses_3c99b5043ffeFGeuraVIodT6wM"
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],
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"plan_name": "depth-refinement-robust",
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"agent": "atlas"
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}
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@@ -58,3 +58,9 @@
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- **Documentation as Contract**: Updating the docs *after* implementation revealed that the "Unit Mismatch" section was outdated. Explicitly marking it as "Resolved" preserves the history while clarifying current behavior.
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- **Benchmark Matrix Value**: Documenting the benchmark matrix makes it a first-class citizen in the workflow, encouraging users to empirically verify refinement improvements rather than trusting defaults.
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- **Confidence Weights**: Explicitly documenting this feature highlights the importance of sensor uncertainty in the optimization process.
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## Bug Fix: Variable-Length Residual Vectors
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- Fixed a `ValueError` in `scipy.optimize.least_squares` caused by the residual vector changing length between iterations.
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- The root cause was filtering for valid depth points *inside* the residual function. If a point projected outside the image or had invalid depth in one iteration but not another, the vector length would change, which `least_squares` does not support.
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- Solution: Identify "active" points at the start of refinement (`T_initial`) and use this fixed set of points for all iterations.
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- If a point becomes invalid during optimization (e.g., projects out of bounds), it is now assigned a large constant residual (10.0m) instead of being removed from the vector. This maintains a stable dimensionality while discouraging the optimizer from moving towards invalid regions.
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