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])

tb_random_dropout: input → output

*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*

*Knob a does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

*Knob b does not change the output (measured: identical at 0.1 / 0.5 / 0.9).*

Stages (the ops that come before → this op, left to right):

▸ tb_random_dropout: stages (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ tb_random_dropout: other inputs (docs site)

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。

`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。

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.

Try it in Studio

The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.

img_to_points 0.50 0.50
tb_random_dropout 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

The examples below call the underlying ledger op random_dropout. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).

• augment_pointcloud — py -3.11 examples_3d/augment_pointcloud.py

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

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

Same category (`typed`)

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

*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