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"))
Extract up to max_planes planes one after another by iterated RANSAC (leftover points get −1).
Repeats: apply ransac_fit.ransac_plane to the remaining point set, and if the inlier count of the largest-consensus plane is at least min_inliers, give it a new label and remove it → re-detect on the rest. Separates several floor/wall/stair-like faces in one go (the difference from pcseg, which fits a single plane). Stops as soon as the inliers fall short of min_inliers; the remaining points are residual -1 (spheres and complex objects stay here).
Args:
points: (N,3) point cloud.
thresh: inlier threshold on point-to-plane distance (a distance, must be > 0).
min_inliers: minimum number of inliers to accept a plane (must be >= 3).
max_planes: maximum number of planes to extract (must be >= 1).
iters: number of iterations of each RANSAC.
seed: random seed (deterministic; each plane uses seed+plane index).
Returns:
labels: (N,) int. Assigns 0,1,2,... to planes in detection order (≈ descending consensus size);
residual points belonging to no plane are -1. Empty input gives shape (0,).
• 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.
• object_segmentation — py -3.11 examples_3d/object_segmentation.py
labels as input)fuse_to_voxel · vol_region_props
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.