euclidean_cluster — 3D `segment` op

• Data kinds: points → labels

• Call: import fullseye as fs; fs.ledger.euclidean_cluster(points, tol: 'float', min_size: 'int' = 10) -> 'np.ndarray' (to call the implementation directly, import segment3d; segment3d.euclidean_cluster(points, tol: 'float', min_size: 'int' = 10) -> 'np.ndarray'; from the registry, ops3d.get("euclidean_cluster"))

Usage

Distance clustering by the connected components of the proximity graph at radius tol (−1 = noise).

Groups points that are within tol of each other (transitively) into the same cluster. Spatially separated objects become separate clusters (used to separate "which blob is a graspable object" after ground-plane removal). Connected components smaller than min_size are noise, -1. Labels are 0,1,2,... in descending order of cluster size (deterministic).

Args:

points: (N,3) point cloud.

tol: proximity radius for being considered the same cluster (a distance, must be > 0).

min_size: connected components smaller than this are noise (-1).

Returns:

labels: (N,) int. 0..(n_clusters-1) are clusters, -1 is noise. Empty input gives shape (0,).

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• object_segmentation — py -3.11 examples_3d/object_segmentation.py

Ops the type connects to (they accept `labels` as input)

fuse_to_voxel · vol_region_props

Same category (`segment`)

region_growing · plane_segmentation · vol_watershed

*Provenance: segment3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

euclidean_cluster — 3D segment op