## Task 4 Complete: CLI Flags Added

Successfully added all required CLI flags:
- --verify-depth / --no-verify-depth
- --refine-depth / --no-refine-depth  
- --depth-mode [NEURAL|ULTRA|PERFORMANCE|NONE]
- --depth-confidence-threshold
- --report-csv

All flags appear in --help output.

## Next Steps

Tasks 5 and 6 require integrating depth verification and refinement into the main workflow:
- Task 5: Add depth verification logic to main() function
- Task 6: Add depth refinement logic to main() function
- Task 7: Create unit tests for depth modules

The integration needs to:
1. Parse depth_mode string to sl.DEPTH_MODE enum
2. Pass depth_mode to SVOReader when verify_depth or refine_depth is enabled
3. After computing extrinsics, run depth verification
4. If refine_depth is enabled, run optimization and update results
5. Add verification stats to output JSON
6. Optionally write CSV report

## Task 5 Complete: Depth Verification Integration

- **Refactored `depth_verify.py`**:
  - Implemented `compute_marker_corner_residuals` to return detailed (marker_id, corner_idx, residual) tuples.
  - Added confidence map filtering logic: filters out points where ZED confidence > threshold (lower is better in ZED SDK).
  - Fixed `compute_depth_residual` to handle projection errors and bounds checking robustly.
  - Return type `DepthVerificationResult` now includes list of all residuals for CSV reporting.

- **Updated `svo_sync.py`**:
  - Added `confidence_map` to `FrameData` dataclass.
  - Updated `SVOReader` to retrieve both depth and confidence measures when depth mode is enabled.

- **Integrated into `calibrate_extrinsics.py`**:
  - Stores a "verification sample" (last valid frame with depth) during the processing loop.
  - Post-process verification: After `T_mean` is computed, runs verification using the stored sample and the final consensus pose.
  - Adds `depth_verify` block to the output JSON with RMSE, mean absolute error, and validity counts.
  - Writes detailed CSV report if `--report-csv` is provided.

- **Verification**:
  - `uv run pytest -q` passes (10 tests).
  - LSP checks pass for modified files.
### Type Fixes in aruco/svo_sync.py
- Updated type annotations to use modern Python 3.12 syntax:
  - Replaced `List`, `Dict`, `Optional` with `list`, `dict`, and `| None`.
  - Added missing type arguments to generic classes (e.g., `dict[str, Any]`).
  - Added explicit class attribute annotations for `SVOReader`.
- Verified that `lsp_diagnostics` no longer reports `reportMissingTypeArgument` or `reportDeprecated` for this file.
- Confirmed that `uv run pytest` passes after changes.

## Depth Refinement Integration
- **Pattern**: Post-process refinement is superior to per-frame refinement for extrinsic calibration.
  - **Why**: Per-frame refinement is noisy and computationally expensive. Refining the robust mean pose against a high-quality frame (or average of frames) yields more stable results.
  - **Implementation**: Store a representative frame (e.g., last valid frame with depth) during the loop, then run refinement once on the final aggregated pose.
- **Verification**: Always verify *before* and *after* refinement to quantify improvement.
  - **Metrics**: RMSE of depth residuals, delta rotation/translation.
  - **Guardrails**: Skip refinement if insufficient valid depth points are available (e.g., < 4 points).

## Task 6 Complete: Depth Refinement Integration

- **Refinement Logic**:
  - Implemented post-process refinement: verifies initial pose, refines using L-BFGS-B optimization, then verifies refined pose.
  - Updates JSON output with `refine_depth` stats and `depth_verify_post` metrics.
  - Calculates and reports RMSE improvement.
  - Uses refined pose for final CSV report if enabled.

- **Code Quality**:
  - Cleaned up loop logic to remove per-frame refinement attempts (inefficient).
  - Added proper type hints (`List`, `Dict`, `Any`, `Optional`, `Tuple`) to satisfy LSP.
  - Removed agent memo comments.
  - Verified with `pytest` (18 passed) and `calibrate_extrinsics.py --help`.

- **Key Implementation Details**:
  - Refinement only runs if sufficient valid depth points (>4) exist.
  - Refined pose (`T_refined`) replaces the original RANSAC mean pose in the output JSON if refinement is successful.
  - Original RANSAC stats remain in `stats` field, while refinement deltas are in `refine_depth`.