gradient3d — 3D edges op

• Data kinds: voxel → gradient

• Call: import fullseye as fs; fs.ledger.gradient3d(vol, sigma: 'float' = 1.0) (to call the implementation directly, import edges3d; edges3d.gradient3d(vol, sigma: 'float' = 1.0); from the registry, ops3d.get("gradient3d"))

Usage

Compute the central-difference gradient after Gaussian smoothing.

Parameters

vol : (D,H,W) array

Grey voxels.

sigma : float

Standard deviation of the Gaussian smoothing (in voxels). 0 means no smoothing (raw central differences).

Returns

gmag : (D,H,W) float64

Gradient magnitude ||∇I||.

gvec : (D,H,W,3) float64

Gradient vector. Components are (∂/∂axis0, ∂/∂axis1, ∂/∂axis2) = (depth, height, width).

Notes

Smoothing uses scipy.ndimage.gaussian_filter(mode="nearest"), differentiation uses np.gradient(spacing=1). np.gradient uses central differences in the interior and one-sided differences only at the boundary.

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)

• edges_3d — py -3.11 examples_3d/edges_3d.py

Ops the type connects to (they accept gradient as input)

fuse_to_voxel

Same category (edges)

canny3d · log_zero_crossings · link_edges · edge_points

*Provenance: edges3d.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.