typed op• Data kinds: points → signal
• Call: fullseye.apply(img, "tb_geodesic_distances", 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_geodesic_distances: knob a sweep (docs site)
*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*
Stages (the ops that come before → this op, left to right):
▸ tb_geodesic_distances: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_geodesic_distances: other inputs (docs site)
Geodesic distances from source to all points (Dijkstra on a kNN graph). → (N,) float (unreachable points are inf).
> The detailed description below is the original text — the summary and the headings are translated.
`knn_graph(points, k)` で作った k 近傍グラフ(辺の重み = 点間の Euclid 距離 = 弦長)を
`directed=False で無向化し、scipy.sparse.csgraph.dijkstra` で単一始点最短路を解く。
`d[i] は source から点 i までのグラフ上の経路長で d[source] = 0`、source と繋がっていない
連結成分の点は `inf`。単位は座標の単位そのまま。
• `points`: (N,3) の点群(float64 に変換)。
• `source`: 始点の添字(0..N-1 の整数。範囲外は scipy 側で例外)。
• `k: 近傍数(既定 8)。小さいとグラフが分断されて inf` が増え、大きいと離れた面どうしを
直結する「近道」が生まれて曲面に沿わない距離になる(薄い板の表裏、折り返した面など)。
精度: 辺が弦長なので弧をわずかに過小評価する一方、経路のジグザグが過大評価を生む(モジュール
docstring の Bernstein らの挟み込み評価を参照)。三角メッシュがあるなら近傍数に依存しない
`geodesic_mesh を使う。この距離で均等に間引くには farthest_point_sampling`。
2-D 進化レジストリへ橋渡しした 3d の op `geodesic_distances。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 k(既定 8)を振る。b` は未使用。
• 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_geodesic_distances 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op geodesic_distances. 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).
• geodesic_distance — py -3.11 examples_3d/geodesic_distance.py
signal as input)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d
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.