metrics op• Data kinds: points × points → measurement
• Call: import fullseye as fs; fs.ledger.fscore(a, b, tau) (to call the implementation directly, import metrics3d; metrics3d.fscore(a, b, tau); from the registry, ops3d.get("fscore"))
• Return value through the ledger: fullseye.ledger.fscore(...) returns only the declared out type measurement (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.fscore.raw(...) or call metrics3d.fscore directly.
• Underlying return: (f, precision, recall) → F 値
F-score @ tau = the harmonic mean of precision and recall. → (f, precision, recall). The standard reconstruction metric.
Raises ValueError: if either is empty or not (N,3) (via accuracy/completeness).
Computation (convention: a = reconstruction/estimate, b = reference/GT):
• precision = the fraction of points of a whose nearest-neighbour distance to b is less than tau (< tau, equality not included).
• recall = the fraction of points of b whose nearest-neighbour distance to a is less than tau.
• f = 2 p r / (p + r); if p + r == 0, 0.0 (no division by 0).
Arguments: a, b are (N, 3) / (M, 3) point clouds (no correspondence needed, point counts may differ). tau is the distance threshold (same unit as the coordinates, a positive value is assumed. With tau <= 0 even points at distance 0 do not satisfy < tau and precision = recall = 0. This is not checked).
Returns: 3 float values (f, precision, recall), each in [0, 1]. 1 is a perfect match.
Note: the choice of tau decides the result (1-2 times the voxel size or scan resolution is a guideline). If only one cloud is dense, precision and recall diverge — seeing that asymmetry is the value of this metric; if 1 number suffices, use chamfer_distance.
• 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 · 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.