tb_voxel_grid_downsample — 2D `typed` op

• Data kinds: points → points

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

tb_voxel_grid_downsample: 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_voxel_grid_downsample: stages (docs site)

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

▸ tb_voxel_grid_downsample: other inputs (docs site)

Usage

Thins the point cloud with a grid of cell size voxel_size, aggregating each cell into a single centroid point (deterministic).

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

空間を一辺 `voxel_size` の立方体セルに区切り、同じセルに落ちた点をその重心

1 点で代表させる。密度ムラを均し、下流(ICP・特徴量)の計算量を点数で抑える標準手法。

出力順はボクセル座標の辞書順で固定(同じ入力なら常に同じ出力=決定論的)。

Parameters

----------

points : array_like, shape (N, 3)

入力点群。

voxel_size : float

セルの一辺(> 0)。大きいほど強く間引く。

Returns

-------

ndarray, shape (M, 3)

各占有セルの重心(M <= N)。すべて入力の軸並行 bounding box 内に収まる。

Notes

-----

`voxel_size <= 0` は ValueError。空入力は空 (0,3) を返す(graceful)。

重心はセル内の点の平均なので、必ず入力点の凸包(ゆえに bbox)内に入る。

2-D 進化レジストリへ橋渡しした 3d の op `voxel_grid_downsample。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_voxel_grid_downsample 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 voxel_grid_downsample. 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).

• pointcloud_downsampling — py -3.11 examples_3d/pointcloud_downsampling.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_mls_smooth

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

tb_voxel_grid_downsample — 2D typed op