metrics op• Data kinds: points × normals → measurement
• Call: import fullseye as fs; fs.ledger.normal_consistency(points_a, normals_a, points_b, normals_b) (to call the implementation directly, import metrics3d; metrics3d.normal_consistency(points_a, normals_a, points_b, normals_b); from the registry, ops3d.get("normal_consistency"))
Normal agreement over nearest-neighbour correspondences = mean|cos(na, nb)| (orientation ignored). → [0,1]. 1 = perfect agreement.
Raises ValueError: if a point cloud is empty or not (N,3), or the normals are not 1-to-1 with the points.
Computation: for each point of points_a, take the nearest neighbour in points_b (cKDTree, k=1), normalise that point's normal nb and its own normal na to unit length (divide by norm + 1e-12) and average |na · nb|. Since the absolute value is taken, a mismatch in normal orientation (front/back) is ignored. Asymmetric (only the one direction a → b. If the reverse is needed, swap the arguments and call again).
Arguments: points_a (N, 3), normals_a (N, 3), points_b (M, 3), normals_b (M, 3). It checks that all 4 are non-empty (*, 3) and that the row count of the normals matches the row count of the corresponding point cloud (violations raise ValueError). A zero normal is divided by 1e-12 and contributes almost 0 (not an error).
Returns: Python float, [0, 1]. 1 = normals parallel in all correspondences. For random normal pairs the expected value in 3-D is about 0.5 (the mean of the absolute cos).
Note: it does not look at positional closeness (even a distant nearest neighbour only has its normal compared). To evaluate together with position, combine it with chamfer_distance / fscore. For a cloud without normals, create them with estimate_point_normals (local PCA) before passing it in.
• blender_interop — Using Blender alongside — build the shape, measure it with fullseye (pitfalls: axes, units, ground truth)
• measurement_uncertainty — Measurement uncertainty and calibration — what it takes to claim you are measuring
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• metrics_eval — py -3.11 examples_3d/metrics_eval.py
measurement as input)vol_gaussian_psf · fuse_to_voxel · fresnel_reflectance · snell_angle
metrics)chamfer_distance · hausdorff_distance · m3c2_distance · fscore · rmse_correspondence · voxel_iou · pose_error
*Provenance: metrics3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.