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).
> The detailed description below is the original text — the summary and the headings are translated.
各点を k 近傍グラフ上で BFS 成長させ、隣接点 q を「法線 n_p と n_q の成す角が
`angle_thresh_deg` 未満」のときだけ同領域に加える。平面内の法線はほぼ平行なので
同一領域に連結し、向きの違う面の境界では角度が開いて連結が切れる → 面ごとに別領域。
法線は符号不定(PCA 由来)なので `|n_p·n_q|` で判定(表裏を同一視)。
Args:
points: (N,3) 点群。
normals: (N,3) 単位法線。None なら :func:pointcloud.estimate_normals で PCA 推定。
angle_thresh_deg: 隣接法線角度の許容上限[度]。(0,180) の範囲。
k: 近傍数(kNN グラフの次数)。
Returns:
labels: (N,) int。連結平滑領域ごとに 0,1,2,... を付与。**min_region_size 未満の
小領域(孤立点・向き不一致のゴミ)は -1(ノイズ/未割当)** = 統一契約(-1=ノイズ)に従う。
空入力は shape (0,) を返す。
2-D 進化レジストリへ橋渡しした 3d の op `region_growing。実装は同じで、呼び出し規約だけ op(v, a, b) に合わせてある。a が angle_thresh_deg(既定 15)、b が k`(既定 20)を振る。
• 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.