tb_random_dropout — 2D `typed` op

• Data kinds: points → points

• Call: fullseye.apply(img, "tb_random_dropout", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

*No figure: this op takes points as input. A Studio program starting from an image cannot reach that type — see the runnable examples below for how it is used.*

Usage

Randomly removes a `ratio fraction of the points and returns (kept, kept_idx)` (mimicking missing data).

> 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。

2-D 進化レジストリへ橋渡しした 3d の op `random_dropout。実装は同じで、呼び出し規約だけ op(v, a, b) に合わせてある。この op に調整点は無く、a も b` も使われない。

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)

• (none yet)

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

identity · tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal

Same category (`typed`)

tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

tb_random_dropout — 2D typed op