metrics op• Data kinds: voxel × voxel → measurement
• Call: import fullseye as fs; fs.ledger.voxel_iou(vol_a, vol_b, iso=0.5) (to call the implementation directly, import metrics3d; metrics3d.voxel_iou(vol_a, vol_b, iso=0.5); from the registry, ops3d.get("voxel_iou"))
The IoU (intersection over union) of voxel occupancy. → [0,1]. Volumetric agreement.
Both volumes must have the same shape. Different shapes would appear to match via numpy broadcasting and silently return a wrong IoU (e.g. (10,1,10) vs (1,10,10) → 1.0), so, fail-closed, a shape mismatch is rejected with ValueError.
Computation: binarise both volumes with >= iso (iso default 0.5, equality included), then |A∩B| / |A∪B|. If the union is empty (both have zero occupancy), returns 1.0 ("both empty" counts as a match; not an error).
Args: vol_a, vol_b are arrays of the same shape (not limited to 3-D; the number of dimensions is not checked; bool / 0-1 / density grid are all fine). iso is the threshold regarded as occupied; when used on the density grid of fuse_to_voxel or points_to_voxel (values are point counts), set iso=1 or higher explicitly. NaN becomes False (unoccupied) under >=.
Returns: Python float, [0, 1]. 1 = perfect match, 0 = no overlap.
Note: volumes not on the same grid (bounds and size) cannot be compared — pass the same bounds to fuse_to_voxel / points_to_voxel. Between thin shells, a shift of just 1 voxel drops the IoU sharply, so for evaluating surfaces chamfer_distance / fscore are better suited. For objects with small volume, Dice (voxel_dice, a function of the same module) is the conventional choice.
• 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 · normal_consistency · 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.