plane_segmentation — 3D `segment` op

• Data kinds: points → labels

• Call: import fullseye as fs; fs.ledger.plane_segmentation(points, thresh: 'float', min_inliers: 'int', max_planes: 'int' = 5, iters: 'int' = 300, seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import segment3d; segment3d.plane_segmentation(points, thresh: 'float', min_inliers: 'int', max_planes: 'int' = 5, iters: 'int' = 300, seed: 'int' = 0) -> 'np.ndarray'; from the registry, ops3d.get("plane_segmentation"))

Usage

Extract up to max_planes planes one after another by iterated RANSAC (leftover points get −1).

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

残り点集合に :func:ransac_fit.ransac_plane を掛け、その最大 consensus 平面の

inlier 数が `min_inliers` 以上なら新ラベルを与えて除去 → 残りで再検出、を繰り返す。

複数の床/壁/階段状の面を一度に分離する(単一平面適合の pcseg との差)。inlier が

`min_inliers` に満たなくなった時点で停止し、以降の点は残差 -1(球や複雑物体はここに残る)。

Args:

points: (N,3) 点群。

thresh: 点-平面距離の inlier しきい値(距離、要 > 0)。

min_inliers: 平面として採用する最小 inlier 数(要 >= 3)。

max_planes: 抽出する平面の最大枚数(要 >= 1)。

iters: 各 RANSAC 反復数。

seed: 乱数シード(決定論。各平面で seed+平面index を使う)。

Returns:

labels: (N,) int。検出順(=consensus 大きい順に近い)に 0,1,2,... を平面へ付与、

どの平面にも属さない残差点は -1。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 · euclidean_cluster · vol_watershed

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

plane_segmentation — 3D segment op