fscore — 3D `metrics` op

• Data kinds: points × points → measurement

• Call: import fullseye as fs; fs.ledger.fscore(a, b, tau) (to call the implementation directly, import metrics3d; metrics3d.fscore(a, b, tau); from the registry, ops3d.get("fscore"))

• Return value through the ledger: fullseye.ledger.fscore(...) returns **only the declared out type measurement** (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.fscore.raw(...) or call metrics3d.fscore directly.

• Underlying return: (f, precision, recall) → F 値

Usage

F-score @ tau = the harmonic mean of precision and recall. → (f, precision, recall). The standard reconstruction metric.

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

Raises ValueError: どちらかが空 or (N,3) でない場合(accuracy/completeness 経由)。

計算(`a = 再構成/推定、b` = 参照/GT の慣習):

• `precision = a の点のうち、b への最近傍距離が tau` 未満

(`< tau`、等号は含まない)の割合。

• `recall = b の点のうち、a への最近傍距離が tau` 未満の割合。

• `f = 2 p r / (p + r)、p + r == 0 なら 0.0`(0 除算にしない)。

引数: `a, b は (N, 3) / (M, 3)` 点群(対応不要、点数は異なってよい)。

`tau は距離閾値(座標と同じ単位、正の値を想定。tau <= 0` だと距離 0 の点も

`< tau` にならず precision = recall = 0 になる。検査はしない)。

返り値: `(f, precision, recall) の 3 つの float、それぞれ [0, 1]`。

1 が完全一致。

注意: `tau` の選び方が結果を決める(voxel サイズ・スキャン分解能の 1〜2 倍が

目安)。片方の雲だけ密だと precision と recall が乖離する — その非対称性を見る

のがこの指標の価値で、1 数字で良ければ `chamfer_distance`。

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)

• measurement_uncertainty — 計測の不確かさと校正の知識 — 「測れている」を主張するために

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)

• metrics_eval — py -3.11 examples_3d/metrics_eval.py

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

vol_gaussian_psf · fuse_to_voxel · fresnel_reflectance · snell_angle

Same category (`metrics`)

chamfer_distance · hausdorff_distance · m3c2_distance · rmse_correspondence · normal_consistency · voxel_iou · pose_error

*Provenance: metrics3d.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

fscore — 3D metrics op