typed op• Data kinds: points → volume
• Call: fullseye.apply(img, "tb_region_growing", 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_region_growing: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_region_growing: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_region_growing: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_region_growing: other inputs (docs site)
Motion (GIF: frames / views / slices in turn. The still figure is the complete one; the GIF is supplementary):
Grows regions by normal similarity, assigning the same label to connected smooth regions (a variant without a curvature gate).
Each point grows by BFS on the k-nearest-neighbour graph, and a neighbour q is added to the same region only when "the angle between the normals n_p and n_q is less than angle_thresh_deg". Normals within a plane are nearly parallel, so they connect into one region; at the boundary between faces of different orientation the angle opens and the connection breaks → a separate region per face. Normals have no definite sign (they come from PCA), so the test uses |n_p·n_q| (front and back are treated as the same).
Args:
points: (N,3) point cloud.
normals: (N,3) unit normals. If None, estimated by PCA with pointcloud.estimate_normals.
angle_thresh_deg: upper limit of the angle between neighbouring normals [degrees]. In the range (0,180).
k: number of neighbours (degree of the kNN graph).
Returns:
labels: (N,) int. 0,1,2,... assigned to each connected smooth region. Small regions below min_region_size (isolated points, misoriented debris) get -1 (noise / unassigned), following the common contract (-1 = noise).
Empty input returns shape (0,).
The 3d op region_growing 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 angle_thresh_deg (default 15) and b sweeps k (default 20).
• 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_region_growing 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op region_growing. 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).
• sensor_seg — py -3.11 examples_3d/sensor_seg.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.