geodesic op• Data kinds: points → indices
• Call: import fullseye as fs; fs.ledger.farthest_point_sampling(points: numpy.ndarray, n: int, k: int = 8, start: int = 0) -> numpy.ndarray (to call the implementation directly, import geodesic3d; geodesic3d.farthest_point_sampling(points: numpy.ndarray, n: int, k: int = 8, start: int = 0) -> numpy.ndarray; from the registry, ops3d.get("farthest_point_sampling"))
Farthest-point sampling by geodesic distance (uniform thinning). → the selected index sequence (n,) int.
A greedy method (FPS) that takes start as the first representative point and adds, 1 at a time, "the point with the largest geodesic distance to the set of already chosen points". Distances are measured by Dijkstra on the undirected kNN graph of knn_graph(points, k); the distance to the chosen set is kept as mind, the element-wise minimum of the single-source distances of the representative points, updated by mind = min(mind, d_new) each time 1 representative point is added. Each 1 representative point costs 1 Dijkstra run, so the cost is n runs of single-source shortest paths. No random numbers are used; it is deterministic.
• points: (N,3) point cloud. n: number of points wanted. Values above N are rounded to N, negative values to 0 (0 gives an empty array).
• k: number of neighbours of the kNN graph (default 8). start: first representative point (wrapped into range by start % N).
• Returns the index sequence in the order chosen (start first). points[idx] gives the representative points.
Pitfall: if the graph splits into several connected components, unreachable points have distance inf, so an unreached component is chosen first (argmax picks up inf). This gives a "take distant clusters first" behaviour, so if you want to subsample each component evenly, split them first with euclidean_cluster or similar.
• 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.
• geodesic_distance — py -3.11 examples_3d/geodesic_distance.py
• pointcloud_downsampling — py -3.11 examples_3d/pointcloud_downsampling.py
indices as input)geodesic)geodesic_distances · geodesic_mesh · knn_graph
*Provenance: geodesic3d.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.