extract_surface_points — 3D `tsdf_fusion` op

• Data kinds: sdf → points

• Call: import fullseye as fs; fs.ledger.extract_surface_points(tsdf: 'np.ndarray', weight: 'np.ndarray', bounds: 'Bounds', res: 'int') -> 'np.ndarray' (to call the implementation directly, import tsdf_fusion; tsdf_fusion.extract_surface_points(tsdf: 'np.ndarray', weight: 'np.ndarray', bounds: 'Bounds', res: 'int') -> 'np.ndarray'; from the registry, ops3d.get("extract_surface_points"))

Usage

Extract surface points (M,3) from the TSDF zero crossing (no marching cubes needed; linear interpolation).

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

観測済み(weight>0)の隣接 voxel 対で TSDF 符号が変わる辺を、その 2 中心の間で

`alpha = t_a/(t_a - t_b)` により線形補間して交点(表面点)を出す。両端とも weight>0 の

辺のみ採用(未観測の初期値 1.0 との偽の交差を作らない=honest)。x/y/z 3 軸の全辺を走査。

交差が無ければ空 (0,3) を返す(詐称せず honest な空返し)。

Background guides (the physics and conventions behind this op)

• depth_sensors — 深度センサの知識 — 測距原理・実機の値・欠測の出方

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)

• transforms_repr — py -3.11 examples_3d/transforms_repr.py

• tsdf_fusion_demo — py -3.11 examples_3d/tsdf_fusion_demo.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 (`tsdf_fusion`)

fuse · integrate

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

extract_surface_points — 3D tsdf_fusion op