typed op• Data kinds: points → feature
• Call: fullseye.apply(img, "tb_reflection_symmetry_score", 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_reflection_symmetry_score: stages (docs site)
Reflection symmetry score = chamfer(mirrored, original) / median nearest-neighbor spacing (smaller = more symmetric, scale-invariant). -> float.
The point cloud is mirrored across the plane with reflect_points, and the symmetric Chamfer distance to the original cloud (chamfer_distance:
the mean of the means of the nearest-neighbour distances in both directions) is divided by the median nearest-neighbour spacing of the original cloud. It is the dimensionless quantity "by how many point spacings the mirror image is off from the original points", so multiplying the coordinates by a constant does not change the value.
• points: (N,3) point cloud. plane_point / plane_normal: candidate plane (the normal is normalised internally).
• fail-closed: the point cloud is empty or not (N,3) (chamfer_distance raises ValueError), or all points coincide so the spacing cannot be defined (ValueError). Only when duplicate points make the median spacing 0 is the RMS radius from the centroid × 1e-12 used as a floor.
Reading note: even for an exactly symmetric shape, the mirrored points do not land exactly on the original sample points, so the score does not become 0; its floor is about one point spacing (for measured values see the table of detect_reflection_symmetry). Rather than accepting or rejecting by a threshold, sweep several candidates and look at the minimum and its gap to the runner-up (margin). To try candidate planes other than the 3 PCA axes, sweep this function directly over the plane parameters.
The 3d op reflection_symmetry_score 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_reflection_symmetry_score 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op reflection_symmetry_score. 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).
• dl_mesh_symmetry — py -3.11 examples_3d/dl_mesh_symmetry.py
• reflection_symmetry — py -3.11 examples_3d/reflection_symmetry.py
feature as input)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.