typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_mls_smooth", 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_mls_smooth: knob a sweep (docs site)
*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_mls_smooth: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_mls_smooth: other inputs (docs site)
Projects each point onto a local polynomial surface to remove noise (Moving Least Squares smoothing).
For each point, gathers the neighbours within radius radius, estimates the local plane (normal n and the 2 axes of the tangent plane) by weighted PCA, and fits a polynomial surface of degree order to the neighbours' "tangent-plane coordinates (u,v) → height h along the normal" by Gaussian-weighted least squares. The point of interest itself sits at the origin (0,0) of the local coordinates, so it is moved along the normal by the height of the fitted surface at (0,0) (= the constant term) and thereby projected onto the surface. Only sensor noise is smoothed while the shape of the surface is kept.
points : array_like, shape (N, 3)
Input point cloud.
radius : float
Radius of the neighbourhood sphere (> 0). The support of the local surface.
order : int
Degree of the local polynomial (default 2). The number of terms is (order+1)(order+2)/2.
ndarray, shape (N, 3)
Smoothed point cloud (order and number of points kept). Points whose neighbours are fewer than the polynomial's number of terms stay in place.
This is an approximation method. Points with fewer neighbours than terms / with a degenerate local plane are not moved and keep their original position (a safety measure so that it does not run wild at holes or borders). radius <= 0 raises ValueError; empty input returns empty.
The 3d op mls_smooth 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 order (default 2). 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_mls_smooth 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op mls_smooth. 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_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.