depth_denoise op• Data kinds: depth → depth
• Call: import fullseye as fs; fs.ledger.bilateral_filter_depth(depth: 'np.ndarray', spatial_sigma: 'float', range_sigma: 'float', *, invalid: 'float | None' = 0.0, truncate: 'float' = 3.0) -> 'np.ndarray' (to call the implementation directly, import depth_bilateral; depth_bilateral.bilateral_filter_depth(depth: 'np.ndarray', spatial_sigma: 'float', range_sigma: 'float', *, invalid: 'float | None' = 0.0, truncate: 'float' = 3.0) -> 'np.ndarray'; from the registry, ops3d.get("bilateral_filter_depth"))
A bilateral filter for depth images (edge-preserving denoising). → float64 (H,W).
The range weight is built from differences of the depth itself. Invalid pixels (non-finite or sentinel=invalid) do not contribute as neighbours, and if the center is invalid it keeps its original value (filling holes is the job of fill_holes). spatial_sigma is in pixels, range_sigma in depth-value units (a step > range_sigma is not smoothed across, while noise with std < range_sigma is smoothed). With truncate=3 the Gaussian window radius = ceil(3*spatial_sigma).
• blender_interop — Using Blender alongside — build the shape, measure it with fullseye (pitfalls: axes, units, ground truth)
• 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.
• depth_denoise — py -3.11 examples_3d/depth_denoise.py
depth as input)depth_to_points · tsdf_from_depth · to_points · fuse_to_voxel · depth_to_organized_points · normals_from_depth · occlusion_edges · bearing_angle_image
depth_denoise)*Provenance: depth_bilateral.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.