registration_recall — 3D `registration_metrics` op

• Data kinds: points × points → measurement

• Call: import fullseye as fs; fs.ledger.registration_recall(source, target, gt_transform, est_transform, thresh: 'float', *, corr_thresh: 'float | None' = None) -> 'float' (to call the implementation directly, import registration_eval; registration_eval.registration_recall(source, target, gt_transform, est_transform, thresh: 'float', *, corr_thresh: 'float | None' = None) -> 'float'; from the registry, ops3d.get("registration_recall"))

Usage

3DMatch-style per-pair registration success = 1.0 (success) / 0.0 (failure).

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

GT 変換 `gt_transform` で source を target フレームへ写し、target 側の最近傍

(距離 < `corr_thresh`、既定は thresh)を GT 対応(=重なり)として張る。その

対応を 推定変換 `est_transform で写した RMSE が thresh` を切れば成功。

`source/target` は index 対応でなくてよい(対応は GT から張るため target の

並びに依存しない)。GT 重なりが 1 点も無ければ成否は未定義 → `nan`(honest)。

データセット全体の Registration Recall はこの per-pair 指標(重なり有ペア上)の平均。

形状不正は ValueError(fail-closed)。

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)

• reg_eval — py -3.11 examples_3d/reg_eval.py

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

vol_gaussian_psf · fuse_to_voxel · fresnel_reflectance · snell_angle

Same category (`registration_metrics`)

inlier_ratio · rmse_inliers · rotation_translation_error

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

registration_recall — 3D registration_metrics op