compute_fpfh — 3D `feature_register` op

• Data kinds: points × normals → descriptor

• Call: import fullseye as fs; fs.ledger.compute_fpfh(points, normals, k=60, n_bins=11) (to call the implementation directly, import feat_fpfh; feat_fpfh.compute_fpfh(points, normals, k=60, n_bins=11); from the registry, ops3d.get("compute_fpfh"))

Usage

Compute the FPFH descriptor (N, 3*n_bins) (Rusu 2009).

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

1) SPFH: 各点 p と近傍 k 点の対に (α,φ,θ) を求め、各特徴を n_bins ビンでヒストグラム化。

2) FPFH(p) = SPFH(p) + (1/k)Σ_j (1/d_pj) SPFH(j): 近傍 SPFH を距離重みで合成。

3 サブヒストグラムを各々 L1 正規化して連結(既定 33 次元)。角特徴は剛体不変。

引数: points (N,3), normals (N,3), k(FPFH 近傍数), n_bins(1特徴あたりのビン数)。

返り値: (N, 3*n_bins) の記述子行列。

Raises ValueError: points/normals が (N,3) でない・行数不一致・非有限・N<4

(k-NN が k>=3 を要求するため)。

Background guides (the physics and conventions behind this op)

• blas_threads_and_memory — 行列分解が遅い理由の知識 — BLAS スレッド・キャッシュ・メモリ配置

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)

• fpfh_correspondence — py -3.11 examples_3d/fpfh_correspondence.py

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

fuse_to_voxel · shape_distance

Same category (`feature_register`)

harris3d_keypoints · iss_keypoints · shot_descriptor · register_spin · register_fpfh · register_shot

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

compute_fpfh — 3D feature_register op