metrics op• Data kinds: points × points → measurement
• Call: import fullseye as fs; fs.ledger.chamfer_distance(a, b, squared=False) (to call the implementation directly, import metrics3d; metrics3d.chamfer_distance(a, b, squared=False); from the registry, ops3d.get("chamfer_distance"))
The symmetric Chamfer distance = 0.5*(mean_a min_b + mean_b min_a). → scalar. Smaller is a closer match.
Raises ValueError: if either is empty or not (N,3) (the mean of an empty set = a silent NaN).
Computation: with scipy.spatial.cKDTree, take the nearest-neighbour distance d_ab from each point of a to b ((Na,)) and d_ba from b to a, then 0.5 * (mean(d_ab) + mean(d_ba)). With squared=True each distance is raised to the power 2 before averaging (the unit becomes distance^2; the form used in gradient-based optimisation). The default is the raw distance (unit = the coordinate unit).
Input: a, b are point clouds (N, 3) / (M, 3) (point counts may differ, no correspondence needed). Converted to float. Align the coordinate frame and scale of the two beforehand (comparing 2 clouds before registration merely measures the pose difference).
Returns: Python float. 0 for identical point clouds. Range [0, inf), not normalised (it scales with the cloud size, so divide by e.g. the bbox diagonal when comparing).
Note: being a mean, it is less affected by outliers than Hausdorff, but it is sensitive to uneven point density (the mean of the denser side dominates). To equalise density, apply pc_poisson_disk / voxel_grid_downsample first. For threshold-based evaluation use fscore, for the worst case hausdorff_distance, and with known correspondence rmse_correspondence.
• 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.
• itokawa_pose_canonical — py -3.11 examples_3d/itokawa_pose_canonical.py
• itokawa_shape_match — py -3.11 examples_3d/itokawa_shape_match.py
• mesh_lod_download — py -3.11 examples_3d/mesh_lod_download.py
• metrics_eval — py -3.11 examples_3d/metrics_eval.py
• poisson_surface_recon — py -3.11 examples_3d/poisson_surface_recon.py
measurement as input)vol_gaussian_psf · fuse_to_voxel · fresnel_reflectance · snell_angle
metrics)hausdorff_distance · m3c2_distance · fscore · rmse_correspondence · normal_consistency · 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.