typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_estimate_normals", 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_estimate_normals: 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_estimate_normals: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_estimate_normals: other inputs (docs site)
Point-cloud normals unified to point outward (away from the local centroid of neighbors). → (N,3).
Procedure (per point, Python loop): cKDTree takes the k+1 neighbourhood including the point itself (k truncated to N-1), and the eigenvector of the smallest eigenvalue of the scatter matrix local.T @ local of the neighbour coordinates with the query point as origin becomes the normal (unit length). The orientation is "flip if the dot product with the neighbourhood centroid is positive" = aligned to the side away from the neighbourhood centroid.
• Points with fewer than 5 neighbours return the fixed value (0, 0, 1) (not estimated).
• The orientation is a local heuristic: outward for closed convex shapes, but on open surfaces, thin plates and concavities adjacent points can disagree (no global consistency is guaranteed). To align globally, pass the result through orient_normals or use estimate_oriented_normals from the start. For an organized depth image, normals_from_depth is viewpoint-oriented and fast.
• Returns float64 (N,3). k defaults to 25. Deterministic.
• Internally it goes through the same computation as principal_curvatures (even normal estimation runs as far as the quadric fit), so it is slow for many points.
The 3d op estimate_normals 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 25). 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_estimate_normals 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op estimate_normals. 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).
• cylinder_axis_metrology — py -3.11 examples_3d/cylinder_axis_metrology.py
• feature_register — py -3.11 examples_3d/feature_register.py
• oriented_normals — py -3.11 examples_3d/oriented_normals.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.