shape_descriptor op• Data kinds: points → descriptor
• Call: import fullseye as fs; fs.ledger.describe(points, bins: 'int' = 64, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import descriptors3d; descriptors3d.describe(points, bins: 'int' = 64, seed: 'int' = 0) -> 'np.ndarray'; from the registry, ops3d.get("describe"))
A global shape descriptor concatenating D2 + A3 + extent.
A 1-D float64 vector of length 2*bins + 3. The order is [d2(bins), a3(bins), extent(3)]. Each sub-block is already normalised to sum 1, so when comparing with the L1 distance the 3 indices contribute with roughly equal weight.
points : array_like, shape (N, 3)
Point cloud. N >= 3 is required.
bins : int
Number of bins for each of D2 / A3.
seed : int
Random sampling seed for D2 / A3 (the same seed is passed to both).
np.ndarray, shape (2*bins + 3,)
• 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.
• denoise_evolution — py -3.11 examples_3d/denoise_evolution.py
• shape_desc_pose — py -3.11 examples_3d/shape_desc_pose.py
• shape_retrieval — py -3.11 examples_3d/shape_retrieval.py
descriptor as input)fuse_to_voxel · shape_distance
shape_descriptor)d2_distribution · a3_distribution · extent_signature · shape_distance
*Provenance: descriptors3d.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.