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).

> The detailed description below is the original text — the summary and the headings are translated.

互いに `tol` 以内の点を(推移的に)同一クラスタへ束ねる。空間的に離れた物体が

別クラスタになる(接地面除去後の「どの塊が掴める物か」の分離に使う)。連結成分のうち

`min_size` 未満のものはノイズとして -1。ラベルはクラスタサイズ降順で 0,1,2,...

(決定論)。

Args:

points: (N,3) 点群。

tol: 同一クラスタとみなす近接半径(距離、要 > 0)。

min_size: これ未満の連結成分はノイズ(-1)。

Returns:

labels: (N,) int。0..(n_clusters-1) がクラスタ、-1 がノイズ。空入力は 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