typed op• Data kinds: points → image
• Call: fullseye.apply(img, "tb_render_point_depth", 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.*
*The output is shown in a viridis-like pseudo-colour (dark purple = low, yellow = high) so that a field of quantities — distance, phase, orientation, depth — can be read.*
*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_render_point_depth: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_render_point_depth: other inputs (docs site)
Point cloud → depth image (z-buffer; each pixel gets the depth of its nearest point). A sample for observation synthesis / appearance inspection.
Takes (u, v, z) from project_points(points, K, R, t) and, for only those points whose round-ed pixel (row=v, col=u) lies inside size=(H, W) and has z > 0, writes them far-to-near so nearer points overwrite (the smallest z remains per pixel). Pixels without a point are 0 (the convention that tsdf_from_depth / depth_to_points treat as invalid). Returns (H, W) float64, in the units of the point coordinates.
• 1 point = 1 pixel, so sparse point clouds end up full of holes (densify first with mesh_to_points). There is no splat radius.
• K is numpy (3,3); R/t are optional.
Downstream: back with depth_to_points, normals with normals_from_depth, tsdf_from_depth.
The 3d op render_point_depth 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 tuning points; 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_render_point_depth 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op render_point_depth. 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).
• sfm_recon — py -3.11 examples_3d/sfm_recon.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_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample · tb_mls_smooth
*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.