typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_radius_outlier_removal", 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_radius_outlier_removal: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_radius_outlier_removal: other inputs (docs site)
Removes points whose number of neighbors within radius radius is below min_neighbors (removal of isolated points).
Places a sphere of radius radius around each point and drops points for which the number of other points inside falls short of min_neighbors as "isolated grains". More local and direct than the statistical method, and the threshold is easy to set when the real scale of the sensor is known.
points : array_like, shape (N, 3)
Input point cloud.
radius : float
Radius of the sphere regarded as the neighbourhood (> 0).
min_neighbors : int
Number of neighbours required to keep a point (itself not counted, default 8).
filtered : ndarray, shape (M, 3)
Surviving points (original order preserved).
keep_mask : ndarray of bool, shape (N,)
Whether to keep each input point (True = keep).
radius <= 0 raises ValueError. Empty input returns empty (graceful).
The 3d op radius_outlier_removal bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). a sweeps min_neighbors (default 8). b is unused.
• 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_radius_outlier_removal 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op radius_outlier_removal. 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).
• pcl_geodesic — py -3.11 examples_3d/pcl_geodesic.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_voxel_grid_downsample
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_voxel_grid_downsample · 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.