typed op• Data kinds: points → volume
• Call: fullseye.apply(img, "tb_points_to_voxel", 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_points_to_voxel: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_points_to_voxel: other inputs (docs site)
Motion (GIF: frames / views / slices in turn. The still figure is the complete one; the GIF is supplementary):
Point cloud (N,3) → density voxel (size³). Splatted via scatter_add, with optional Gaussian smoothing.
> The detailed description below is the original text — the summary and the headings are translated.
bounds=(lo,hi) を与えれば複数雲を同一格子に載せられる(=マッチング前提)。
手順: 各点を `idx = floor((p − lo)/(hi − lo)·(size − 1))` で整数格子に落とし、その voxel に
1 を加算する(値 = その voxel に落ちた点の個数)。`smooth > 0 なら σ=smooth`(voxel 単位)
の gaussian を 3 軸分離 conv で掛ける(半径 `max(1, int(4σ + 0.5))`、端は replicate)。
出力の軸順は 点の列の順そのまま(`points[:, 0]` → 軸 0)で、(depth,row,col) への
並べ替えはしない。
• `bounds: (lo, hi)` の 3 次元ベクトル 2 本。None なら点群自身の min/max(雲ごとに
格子が変わるので、2 つの雲を比べるときは必ず同じ bounds を渡す)。長さ 3 でない・非有限・
`hi <= lo の軸があると ValueError(tsdf 系の ((xmin,xmax),...)` 流儀は長さ 2 として拒否)。
• 範囲外の点は捨てずに 端の voxel へ clip される(端に偽の密度が溜まる)。切り落としたい
なら事前に点群側で除く。
• `size: 一辺の voxel 数。hi − lo` が 0 の軸は 1e-9 に置換されるだけで警告しない。
• 空の点群で bounds=None は numpy の min が例外を出す。
• 返り値: `(size, size, size)` float64 numpy(device で計算しても CPU に戻す)。値は個数
(平滑後は個数の重み分布)で正規化はしない。
後段: `match_points_ncc / signed_distance_field / voxel_to_mesh` の入力に。
2-D 進化レジストリへ橋渡しした 3d の op `points_to_voxel。実装は同じで、呼び出し規約だけ op(v, a, b) に合わせてある。この op に調整点は無く、a も b` も使われない。
• 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_points_to_voxel 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op points_to_voxel. 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).
• sh_descriptor_retrieval — py -3.11 examples_3d/sh_descriptor_retrieval.py
• shape_desc_pose — py -3.11 examples_3d/shape_desc_pose.py
volume as input)identity · vol_gaussian · vol_median · vol_erode · vol_dilate · vol_threshold · vol_reg_dilate · vol_reg_erode
typed)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 · 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.