mesh_to_points — 3D `transform` op

• Data kinds: mesh → points

• Call: import fullseye as fs; fs.ledger.mesh_to_points(vertices, faces, samples=20000, seed=0) (to call the implementation directly, import match3d; match3d.mesh_to_points(vertices, faces, samples=20000, seed=0); from the registry, ops3d.get("mesh_to_points"))

Usage

Mesh (vertices + faces) → a surface point set (area-weighted uniform sampling). The mesh → point-cloud conversion.

> The detailed description below is the original text — the summary and the headings are translated.

手順: 三角形 `tri = vertices[faces] の面積 0.5·|(b−a)×(c−a)| を確率にして samples`

個の面を復元抽出し、各面で `u, v ~ U(0,1)、u + v > 1 なら (1−u, 1−v)` に折り返す

(一様 barycentric)。点 `= a + u(b−a) + v(c−a)。seed で default_rng` を固定するので

同じ引数なら同じ点群。

• `vertices (V,3) float、faces` (F,3) int(0 始まりの頂点 index)。

• 返り値 `(samples, 3) float64。面の数に関係なくちょうど samples` 点。

• 全面が退化(面積和 0)なら確率が NaN になり `rng.choice` が ValueError。

• 法線は付かない(`estimate_point_normals` で付ける)。面積重みなので大きな面ほど点が多く、

頂点密度には依存しない。

後段: `points_to_voxel / icp_point2point_3d / match_pca`。

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• mesh_decimate — py -3.11 examples_3d/mesh_decimate.py

Ops the type connects to (they accept `points` as input)

points_to_voxel · gaussians_to_voxel · estimate_point_normals · to_points · match_points_ncc · match_pca · moment_axes · icp_point2point_3d

Same category (`transform`)

points_to_voxel · gaussians_to_voxel · mesh_to_voxel · depth_to_points · voxel_to_mips · voxel_to_mesh · tsdf_from_depth · signed_distance_field

*Provenance: match3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

mesh_to_points — 3D transform op