typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_jitter", 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_jitter: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_jitter: other inputs (docs site)
Adds isotropic Gaussian noise N(0, sigma) to each point (mimicking sensor position noise).
sigma is the standard deviation in world units (user parameter). If clip is given, the displacement of each component is truncated to [-clip, clip] (suppressing outlier displacements). For large N the noise has mean ≈ 0 and standard deviation ≈ sigma. The return value has the same shape as the input, (N,3).
• sigma < 0 raises ValueError. sigma == 0 or an empty input returns a copy of the input.
• clip, if given, must be > 0 (otherwise ValueError). Truncation is per component, so
the length of the displacement vector is at most √3·clip.
• Deterministic via seed (np.random.default_rng(seed)). Same seed and same shape give the same noise.
• Input must be (N,3) finite values only (a 1-D of length 3 is promoted to 1 point; anything else or NaN is
ValueError). The return value is float64.
• When measuring the noise robustness of registration (register_fpfh etc.), setting sigma as a ratio to the point spacing
(e.g. the grid width of voxel_grid_downsample) makes conditions easier to compare.
The 3d op jitter 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_jitter 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op jitter. 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
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.