ransac_fundamental — MATCHREG `featmatch` op

• Data kinds: table → table

• Call: import fullseye as fs; fs.ledger.ransac_fundamental(matches, thresh=1.0, confidence=0.999, max_iters=5000, score='msac', local_optimization=True, min_inliers=15, seed=0) (to call the implementation directly, import featmatch; featmatch.ransac_fundamental(matches, thresh=1.0, confidence=0.999, max_iters=5000, score='msac', local_optimization=True, min_inliers=15, seed=0); from the registry, opsmatchreg.get("ransac_fundamental"))

• GPU: none (CPU reference implementation only)

Usage

Robustly estimates the fundamental matrix F (x2ᵀ F x1 = 0) from matched pairs. → table (kind="fundamental").

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

8 点の最小標本を正規化 8 点法(twoview.fundamental_8point、rank 2 を強制)で当てはめ、Sampson 距離(エピポーラ線までの幾何距離の 1 次近似)で採点する。採点・適応回数・局所最適化は ransac_homography と同じ。

限界: 平面の場面や純回転では F は一意に決まらない(ホモグラフィ 1 つで説明できる退化)。その判定はしない —— 姿勢まで欲しいなら twoview.recover_pose(平面を拒む)へ。7 点法は使わない(最小標本は 8)。

Args:

matches: features_match の表。

thresh: 内れ値の Sampson 距離の上限 [px]。

confidence, max_iters, score, local_optimization, seed: ransac_homography と同じ。

min_inliers: これ未満なら失敗(ValueError)。

Returns:

dict(kind="fundamental")。F (3,3)(フロベニウスノルム 1、符号は F[2,2] >= 0)/

inliers / n_inliers / inlier_ratio / rms_px(内れ値の Sampson 距離の rms)/

iterations / score / thresh。

Raises:

ValueError: 表の種類が違う / 8 組未満 / 非有限 / 引数が値域外 / 内れ値が min_inliers 未満。

Detailed usage guide

• matching_registration 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)

• (none yet)

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

features_match · ransac_homography · registration_to_flow2d

Same category (`featmatch`)

features_sift · features_orb · features_match · ransac_homography · features_accuracy_table

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

ransac_fundamental — MATCHREG featmatch op