typed op• Data kinds: points → image
• Call: fullseye.apply(img, "tb_synthesize_silhouette", 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_synthesize_silhouette: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_synthesize_silhouette: other inputs (docs site)
Projects a 3-D point cloud onto a (K,R,t) camera and returns a silhouette (H,W bool) where occupied pixels are True.
For GT generation. Projects points (N,3) by X_cam = R X + t and sets to True the pixels where depth>0 and the point falls inside the image. With sparse point clouds the projected image has holes, so by default they are filled (fill, scipy.ndimage.binary_fill_holes) to make a solid foreground mask. It is further dilated by dilate pixels into a silhouette with coverage semantics, "a pixel is foreground if it touches the object even slightly" -- a conservative rounding that guarantees the recall of the visual hull (not missing object voxels) even under discretisation error.
points : (N, 3) array_like Point cloud in world coordinates (samples of the object surface/interior).
K : (3, 3) Intrinsic parameters.
R, t : (3, 3), (3,) World->camera rotation and translation.
size : (H, W) Output image size.
fill : bool Fill the holes of the projected image to make it solid (default True).
dilate : int Number of pixels to dilate as a coverage margin (default 1, 0 disables).
(H, W) bool ndarray Silhouette with foreground True.
The 3d op synthesize_silhouette 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_synthesize_silhouette 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op synthesize_silhouette. 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).
• space_carving — py -3.11 examples_3d/space_carving.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.