feat: add pose-set comparison visualization and clarify conventions

py_workspace/docs/visualization-conventions.md

@@ -322,6 +322,36 @@ system's origin.
 
 ---
 
+## Methodology: Comparing Different World Frames
+
+Since `inside_network.json` (Fusion) and `calibrate_extrinsics.py` (ArUco) use different
+world origins, raw coordinate comparison is meaningless. We validated consistency using
+**rigid SE(3) alignment**:
+
+1.  **Match Serials**: Identify cameras present in both JSON files.
+2.  **Extract Centers**: Extract the translation column `t` from `T_world_from_cam` for
+    each camera.
+    *   **Crucial**: Both systems use `T_world_from_cam`. It is **not** `cam_from_world`.
+3.  **Compute Alignment**: Solve for the rigid transform `(R_align, t_align)` that
+    minimizes the distance between the two point sets (Kabsch algorithm).
+    *   Scale is fixed at 1.0 (both systems use meters).
+4.  **Apply & Compare**:
+    *   Transform Fusion points: `P_aligned = R_align * P_fusion + t_align`.
+    *   **Position Residual**: `|| P_aruco - P_aligned ||`.
+    *   **Orientation Check**: Apply `R_align` to Fusion rotation matrices and compare
+        column vectors (Right/Down/Forward) with ArUco rotations.
+5.  **Up-Vector Verification**:
+    *   Fusion uses Y-Up (gravity). ArUco uses Y-Down (image).
+    *   After alignment, the transformed Fusion Y-axis should be approximately parallel
+        to the ArUco -Y axis (or +Y depending on the specific alignment solution found,
+        but they must be collinear with gravity).
+
+**Result**: The overlay images in `output/` were generated using this aligned frame.
+The low residuals (<2cm) confirm that the internal calibration is consistent, even
+though the absolute world coordinates differ.
+
+---
+
 ## Appendix: Stale README References
 
 The following lines in `py_workspace/README.md` reference removed flags and should be