random_dropout — 3D `augment` op

• Data kinds: points → points

• Call: import fullseye as fs; fs.ledger.random_dropout(points, ratio: 'float', seed: 'int' = 0) -> 'Tuple[np.ndarray, np.ndarray]' (to call the implementation directly, import pcl_augment; pcl_augment.random_dropout(points, ratio: 'float', seed: 'int' = 0) -> 'Tuple[np.ndarray, np.ndarray]'; from the registry, ops3d.get("random_dropout"))

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

Usage

Randomly remove a `ratio fraction of the points and return (kept, kept_idx)` (mimicking dropout).

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

残す点数は `round((1-ratio)*N)。kept_idx` は元配列への昇順インデックスで、

`kept == points[kept_idx]` が厳密に成り立つ。オクルージョン/疎な視点による

点欠損を学習で再現する。`0 <= ratio <= 1` を要求。

`ratio が [0,1] の外なら ValueError。ratio=1 は空 (0,3)` と空インデックス、

`ratio=0 は全点(順序は元のまま)。残す点数は Python の round`(偶数丸め)で決まる

ので `.5 端では偶数側に寄る。seed で permutation` が決まり決定論的。返り値は

`(kept float64 (M,3), kept_idx int64 (M,))`。除去は空間的に一様なので、局所的な

欠損(遮蔽)を模すには `cutout` を使う。

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)

• augment_pointcloud — py -3.11 examples_3d/augment_pointcloud.py

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

points_to_voxel · gaussians_to_voxel · estimate_point_normals · to_points · match_points_ncc · match_pca · moment_axes · icp_point2point_3d

Same category (`augment`)

jitter · random_rotation · random_scale · elastic_deform · cutout

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

random_dropout — 3D augment op