typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_statistical_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.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_statistical_outlier_removal: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_statistical_outlier_removal: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_statistical_outlier_removal: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_statistical_outlier_removal: other inputs (docs site)
Removes points whose k-nearest-neighbor mean distance is a global outlier (statistical outlier removal).
For each point, measures "the mean distance to its k nearest neighbours (excluding itself)" and drops points exceeding mean + std_ratio*std of that distribution over all points as stray points. Effective for cleaning up sparse stray points; points on dense surfaces remain.
points : array_like, shape (N, 3)
Input point cloud.
k : int
Number of neighbours (default 16). If there are few points it is reduced internally to n-1.
std_ratio : float
Looseness of the threshold. The larger, the more points survive (looser removal).
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). points[keep_mask] equals filtered.
With fewer than 3 points (< 3) no statistics can be formed, so all points are kept (graceful).
The 3d op statistical_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 k (default 16), b sweeps std_ratio (default 2).
• 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_statistical_outlier_removal 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op statistical_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_radius_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_radius_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.