match_localize op• Data kinds: voxel × voxel → position
• Call: import fullseye as fs; fs.ledger.match_hough_3d(scene, template, device='cpu', ndir=26, mc=0.05, topk=1, nms=3, subvoxel=True) (to call the implementation directly, import match3d; match3d.match_hough_3d(scene, template, device='cpu', ndir=26, mc=0.05, topk=1, nms=3, subvoxel=True); from the registry, ops3d.get("match_hough_3d"))
• Return value through the ledger: fullseye.ledger.match_hough_3d(...) returns only the declared out type position (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.match_hough_3d.raw(...) or call match3d.match_hough_3d directly.
• GPU: this op has a GPU path (device="cuda")
Generalized Hough 3-D (Ballard R-table voting). The voxel × Hough column.
Formulates GHT natively for the GPU as the sum of correlations per orientation bin:
accumulator A(t) = Σ_bin ( scene_bin ⋆ template_bin ). Each edge votes according to its gradient direction, and missing edges only lower the peak (robust to occlusion and clutter). Unlike shape-based (a single solution of continuous dot products), its distinguishing feature is that it returns the vote accumulator, so NMS yields multiple peaks = multiple instances. Returns [votes, d, h, w] of shape (topk,4) (votes descending).
Procedure: the unit gradients of both volumes (mc is a threshold on the raw output of sobel3d) are quantised to the nearest of ndir reference directions, and for each direction bin "the scene's 2-valued field for that bin ⋆ the template's 2-valued field for that bin" is summed via conv3d. It is divided by the number of valid template edges, so votes are in [0, 1]; 1 means all edges match.
• ndir: 26 or fewer takes the first ndir entries of the list of 26-neighbourhood directions (below 26 the directions are biased); 27 or more is
uniform on a fibonacci sphere. Coarser directions are more tolerant of rotation but also produce more false peaks.
• Each row of the return value (topk, 4) is [votes, z, y, x], votes descending. The coordinates are the scene coordinates of the template centre (T//2);
with subvoxel=True, the centroid of the ±2 neighbourhood.
• nms: each time a peak is taken, a cube of ±nms voxels is squashed to −1 before searching for the next. For nearby
multiple instances, make nms small.
• If the template has no edges, an all-0 (topk,4). Positions where the template does not fit entirely are 0.
Downstream: refine each peak with refine_translation_lk / refine_peak_newton.
• 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.
• matching_localize — py -3.11 examples_3d/matching_localize.py
position as input)refine_peak_newton · refine_translation_lk · refine_lm · fuse_to_voxel
match_localize)match_shape_3d · match_chamfer_3d · match_curvature_3d · match_mip_2d · match_points_ncc
*Provenance: match3d.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.