typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_cutout", 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_cutout: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_cutout: other inputs (docs site)
Removes a spatial axis-aligned box region and returns (kept, kept_idx) (mimicking localized missing data).
One existing point is sampled uniformly as the centre, and all points inside a box of side extent (scalar = cube, or (3,) = side length per axis) are removed. The centre point itself is always inside, so at least 1 point is removed. The removed points always fit in a box of side extent (spatially local = distinguishable from random scattering). kept == points[kept_idx], extent > 0.
Passing a non-positive value or a shape other than (3,) as extent raises ValueError. Empty input returns empty and an empty index array. The centre is the point chosen by rng.integers(n), so even with the same seed a different place is cut out if the order of the points changes. The box test is the closed interval |P - center| <= extent/2. The number of removed points depends on density; if extent is larger than the cloud, all points vanish. Returns (kept float64 (M,3), kept_idx int64 (M,)), in the original order. For uniform loss use random_dropout.
The 3d op cutout 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 tunable parameters; 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_cutout 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op cutout. 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).
• sensor_seg — py -3.11 examples_3d/sensor_seg.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_voxel_grid_downsample
*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.