lidar_projection op• Data kinds: points → image2d
• Call: import fullseye as fs; fs.ledger.project_spherical(points, h_res: 'int' = 1024, v_res: 'int' = 64, v_fov=(-25.0, 15.0)) -> 'np.ndarray' (to call the implementation directly, import spherical_proj; spherical_proj.project_spherical(points, h_res: 'int' = 1024, v_res: 'int' = 64, v_fov=(-25.0, 15.0)) -> 'np.ndarray'; from the registry, ops3d.get("project_spherical"))
Project into the spherical range image of a spinning LiDAR (v_res, h_res). Empty cells are 0; nearer points win (minimum range).
Drops each point into an azimuth (column) × elevation (row) bin and writes the range (slant distance) from the sensor origin. Points outside the elevation band v_fov=(v_min,v_max)[deg], at the origin (r=0), or with non-finite coordinates are dropped (honest drop). If empty or 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) input raises ValueError.
• h_res / v_res: number of columns (equal divisions of 360° azimuth) and rows (equal divisions of the elevation band). ValueError if not positive.
• v_fov: (v_min, v_max) [deg]. ValueError unless v_min < v_max.
The column is floor((atan2(y,x) + π) / 2π · h_res); column 0 is directly behind (-x), h_res//2 is straight ahead (+x), and it increases counter-clockwise. The row is (v_res-1) - floor((θ - v_min)/(v_max - v_min) · v_res) (θ is the elevation [deg]); row 0 is the top of the band (θ=v_max). The pixel value is the slant range sqrt(x²+y²+z²) (in coordinate units). If 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.
• depth_sensors — Depth sensors — ranging principles, real-device figures, how dropouts appear
• 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.
• lidar_projection — py -3.11 examples_3d/lidar_projection.py
image2d as input)fuse_to_voxel · fit_poly_surface · eval_poly_surface · surface_form_error · background_flatten · polar_unwrap · fit_zernike · matcap_shade
lidar_projection)unproject_spherical · project_cylindrical
*Provenance: spherical_proj.py — 3D 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.