typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_random_rotation", 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_random_rotation: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_random_rotation: other inputs (docs site)
Applies a random rotation and returns (rotated, R) (mimicking viewpoint change).
R is orthonormal with det=+1 (rotated = points @ R.T = R acts on each point from the left; the inverse is rotated @ R). With max_angle=None a uniformly random rotation by Shoemake's method; with max_angle given (radians, expected [0, π]) the axis is uniform on the sphere and the angle uniform on [0, max_angle], limiting the rotation angle (arccos((tr R -1)/2) ≤ max_angle is guaranteed exactly).
max_angle < 0 raises ValueError, max_angle=0 gives the identity. The unit is radians (passing degrees makes it orders of magnitude too large). The uniform rotation with max_angle=None routinely produces large rotations up to π, so use max_angle to narrow the range of viewpoint change. The rotation is about the origin, so if the cloud is away from the origin its centroid moves too. The convention of R, rotated = points @ R.T, has the same orientation as dst ≈ src @ R.T + t returned by register_fpfh and register_shot, so the angle error against an estimate can be measured by arccos((tr(R_est·Rᵀ)-1)/2). Deterministic via seed (same seed gives the same R). Returns float64 (N,3) and (3,3).
The 3d op random_rotation 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_random_rotation 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op random_rotation. 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).
• augment_pointcloud — py -3.11 examples_3d/augment_pointcloud.py
• sh_descriptor_retrieval — py -3.11 examples_3d/sh_descriptor_retrieval.py
• shape_retrieval — py -3.11 examples_3d/shape_retrieval.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.