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])
*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)
Thins the point cloud with a grid of cell size voxel_size, aggregating each cell into a single centroid point (deterministic).
Divides space into cubic cells of side voxel_size and represents the points falling into the same cell by their centroid, 1 point. The standard method to even out density variations and bound the downstream cost (ICP, features) by point count. The output order is fixed to the lexicographic order of voxel coordinates (same input always gives the same output = deterministic).
points : array_like, shape (N, 3)
Input point cloud.
voxel_size : float
Side length of a cell (> 0). The larger, the stronger the thinning.
ndarray, shape (M, 3)
Centroid of each occupied cell (M <= N). All lie within the axis-parallel bounding box of the input.
voxel_size <= 0 raises ValueError. Empty input returns an empty (0,3) (graceful). The centroid is the mean of the points in the cell, so it always lies within the convex hull of the input points (hence within the bbox).
The 3d op voxel_grid_downsample bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). This op has no tuning points; neither a nor b is used.
• 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.
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
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
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
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).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.