occlusion_edges — 3D `range_image` op

• Data kinds: depth → image2d

• Call: import fullseye as fs; fs.ledger.occlusion_edges(depth, rel_thresh=0.05) (to call the implementation directly, import range_image; range_image.occlusion_edges(depth, rel_thresh=0.05); from the registry, ops3d.get("occlusion_edges"))

Usage

Detect depth discontinuities (the foreground/background boundary = occlusion edges). → bool HxW.

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

遮蔽エッジは深度の不連続(step)であって傾斜(slope)ではない。一様な傾斜面は

連続で遮蔽を含まないため flag してはならない。一階勾配(=深度/画素)は傾斜そのもので、

段差か斜面かを区別できない(かつ絶対深度の割合と比較するのは次元不整合)。

そこで軸ごとの二階差分(離散ラプラシアン相当) `d[i+1]-2*d[i]+d[i-1]` を使う。

一様傾斜(深度が近傍で線形)なら二階差分 ≈ 0、fronto-parallel な段差では両側で大きな値。

これを局所深度で正規化した相対的な深度ジャンプが rel_thresh を超える画素を境界とする。

Background guides (the physics and conventions behind this op)

• blender_interop — Blender との併用 — 形を作って fullseye で測る(軸・単位・正解データの罠)

• 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)

• range_image — py -3.11 examples_3d/range_image.py

Ops the type connects to (they accept `image2d` as input)

fuse_to_voxel · fit_poly_surface · eval_poly_surface · surface_form_error · background_flatten · polar_unwrap · fit_zernike · matcap_shade

Same category (`range_image`)

depth_to_organized_points · normals_from_depth · bearing_angle_image

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

occlusion_edges — 3D range_image op