fuse — 3D `tsdf_fusion` op

• Data kinds: depth → sdf

• Call: import fullseye as fs; fs.ledger.fuse(depths: 'Sequence[np.ndarray]', Ks: 'Sequence', Rs: 'Sequence', ts: 'Sequence', bounds: 'Bounds', res: 'int', trunc: 'float') -> 'Tuple[np.ndarray, np.ndarray]' (to call the implementation directly, import tsdf_fusion; tsdf_fusion.fuse(depths: 'Sequence[np.ndarray]', Ks: 'Sequence', Rs: 'Sequence', ts: 'Sequence', bounds: 'Bounds', res: 'int', trunc: 'float') -> 'Tuple[np.ndarray, np.ndarray]'; from the registry, ops3d.get("fuse"))

Usage

Fuse a depth series into one TSDF volume with new_volume + integrate. Returns (tsdf, weight).

Each frame's (K,R,t) is world→camera. When several views observe the same surface, the weights accumulate, and faces invisible in a single frame (self-occluded) are filled in by other views.

• depths: list of (H,W) depth images (camera Z depth; pixels ≤0 or non-finite are treated as unobserved). Sizes may differ between frames.

• Ks / Rs / ts: per-frame (3,3) intrinsic matrix, (3,3) rotation, (3,) translation (X_cam = R X + t).

• bounds: world region ((xmin,xmax),(ymin,ymax),(zmin,zmax)) (max > min on every axis).

• res: number of voxels per side (positive int). The volume is res×res×res, axis order (x,y,z) with indexing='ij'.

• trunc: truncation distance (world units, positive finite value). A few times the voxel size is a guideline; too small and observations do not land on both sides of the surface, too large and the front and back of thin structures mix.

Returns: tsdf (res,res,res) float32 (unobserved +1.0, positive in front of the surface and negative behind, [-1,1]) and weight (res,res,res) float32 (observation count). Surface points are extracted with extract_surface_points(tsdf, weight, bounds, res).

fail-closed: 0 frames, mismatched list lengths, degenerate bounds, and non-positive res / trunc raise ValueError. For details of integration (the rule that occluded regions are not updated) see integrate.

Background guides (the physics and conventions behind this op)

• depth_sensors — Depth sensors — ranging principles, real-device figures, how dropouts appear

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)

• tsdf_fusion_demo — py -3.11 examples_3d/tsdf_fusion_demo.py

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

sdf_to_occupancy · fuse_to_voxel · integrate · extract_surface_points · query_distance · sdf_union · sdf_intersect · sdf_subtract

Same category (`tsdf_fusion`)

integrate · extract_surface_points

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

fuse — 3D tsdf_fusion op