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"))
Extract surface points (M,3) from the TSDF zero crossing (no marching cubes needed; linear interpolation).
For each edge between adjacent observed (weight>0) voxels where the TSDF sign changes, linearly interpolates between the 2 centres with alpha = t_a/(t_a - t_b) to produce the crossing (surface point). Only edges with weight>0 at both ends are used (no false crossings against the unobserved initial value 1.0 = honest). All edges along the 3 axes x/y/z are scanned. If there are no crossings, an empty (0,3) is returned (an honest empty return, nothing faked).
• depth_sensors — Depth sensors — ranging principles, real-device figures, how dropouts appear
• 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.
• transforms_repr — py -3.11 examples_3d/transforms_repr.py
• tsdf_fusion_demo — py -3.11 examples_3d/tsdf_fusion_demo.py
points as input)points_to_voxel · gaussians_to_voxel · estimate_point_normals · to_points · match_points_ncc · match_pca · moment_axes · icp_point2point_3d
tsdf_fusion)*Provenance: tsdf_fusion.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.