typed op• Data kinds: points → image
• Call: fullseye.apply(img, "tb_project_spherical", 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_project_spherical: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_project_spherical: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_project_spherical: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_project_spherical: other inputs (docs site)
Project onto a spherical range image for rotating LiDAR (v_res, h_res). Empty cells = 0, nearest point wins (minimum range).
Each point is dropped into an azimuth (column) × elevation (row) bin, and its range (slant distance) from the sensor origin is written. Points outside the elevation band v_fov=(v_min,v_max)[degrees], at the origin (r=0), or with non-finite coordinates are dropped (honest drop). If the input is empty / everything is dropped, an all-zero image is returned (= nothing is seen, honest).
• points: (N,3), relative to the sensor origin with x = forward, y = left, z = up. Non-(N,3) raises ValueError.
• h_res / v_res: number of columns (equal division of 360° of azimuth) / rows (equal division of the elevation band). Non-positive raises ValueError.
• v_fov: (v_min, v_max) [degrees]. Unless v_min < v_max, ValueError.
The column is floor((atan2(y,x) + π) / 2π · h_res): column 0 is straight behind (-x), h_res//2 is straight ahead (+x), increasing counter-clockwise. The row is (v_res-1) - floor((θ - v_min)/(v_max - v_min) · v_res) (θ is the elevation [degrees]), with row 0 at the top of the band (θ=v_max). The pixel value is the slant range sqrt(x²+y²+z²) (in coordinate units). When several points fall into the same cell, np.minimum.at keeps the minimum range (the farther points are lost). The inverse is unproject_spherical (pass the same v_fov). The variant that slices layers by height is project_cylindrical.
The 3d op project_spherical 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 h_res (default 1024) and b sweeps v_res (default 64).
• 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_project_spherical 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op project_spherical. 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).
• lidar_projection — py -3.11 examples_3d/lidar_projection.py
image as input)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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.