typed op• Data kinds: points → volume
• Call: fullseye.apply(img, "tb_plane_segmentation", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_plane_segmentation: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_plane_segmentation: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_plane_segmentation: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_plane_segmentation: other inputs (docs site)
Motion (GIF: frames / views / slices in turn. The still figure is the complete one; the GIF is supplementary):
Iteratively extracts up to max_planes planes via repeated RANSAC (remaining residual points are -1).
ransac_fit.ransac_plane is applied to the remaining point set; if the inlier count of its largest-consensus plane is at least min_inliers, a new label is assigned and those points are removed → detection is repeated on the rest. Separates several floors / walls / stair-like surfaces at once (the difference from pcseg, which fits a single plane). It stops as soon as the inliers fall short of min_inliers, and the remaining points are residual -1 (spheres and complex objects stay here).
Args:
points: (N,3) point cloud.
thresh: inlier threshold on point-plane distance (a distance, must be > 0).
min_inliers: minimum inlier count 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. 0,1,2,... assigned to planes in order of detection (≈ descending consensus);
residual points belonging to no plane are -1. Empty input gives shape (0,).
The 3d op plane_segmentation bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). a sweeps max_planes (default 5) and b sweeps iters (default 300).
• 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.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_points 0.50 0.50 tb_plane_segmentation 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op plane_segmentation. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• object_segmentation — py -3.11 examples_3d/object_segmentation.py
volume as input)identity · vol_gaussian · vol_median · vol_erode · vol_dilate · vol_threshold · vol_reg_dilate · vol_reg_erode
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D 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.