reconstruct_surface_stereo — MV `stereo` op

• Data kinds: image2d → points

• Call: import fullseye as fs; fs.ledger.reconstruct_surface_stereo(disparity, focal=500.0, baseline=0.1, cx=0.0, cy=0.0) (to call the implementation directly, import reconstruction; reconstruction.reconstruct_surface_stereo(disparity, focal=500.0, baseline=0.1, cx=0.0, cy=0.0); from the registry, opsmv.get("reconstruct_surface_stereo"))

• HALCON equivalent: reconstruct_surface_stereo (the HALCON reference is a useful guide to its meaning and parameters)

• GPU: none (CPU reference implementation only)

Usage

Map the valid pixels of a disparity image to 3-D points.

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

Z = focal * baseline / d、X = (col - cx) * baseline / d、Y = (row - cy) * baseline / d。NaN と inf と 0 の視差は無効として落とす。有効な画素が無いと (0, 3) の空を返す(理由: 無効の画素は点を作らない)。空の入力(disparity が 0 要素)は ValueError。

Detailed usage guide

• machine_vision_classics family guide

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.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• machine_vision_tour — py -3.11 examples/machine_vision_tour.py

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

—

Same category (`stereo`)

binocular_disparity_ms · binocular_distance_ms

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

reconstruct_surface_stereo — MV stereo op