typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_estimate_point_normals", 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_estimate_point_normals: knob a sweep (docs site)
*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_estimate_point_normals: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_estimate_point_normals: other inputs (docs site)
Point cloud (N,3) → unit normals (the smallest eigenvector of the local k-nearest-neighbor covariance = PCA).
> The detailed description below is the original text — the summary and the headings are translated.
FPFH/SHOT/点-面 ICP が要る法線を raw 点群から生成。向きの規約は 2 面:
viewpoint=None(既定)= 重心から外向き(閉じた物体の全周点群向け)/
viewpoint 指定 = 視点(センサ)向き(Hoppe 1992 / PCL 規約。単一視点スキャンの
可視面はセンサ側を向くのが物理的に正しい。pointcloud.estimate_normals と同規約)。
旧版(〜2026-08-30)は viewpoint 指定でも「視点から遠ざける」符号で、単一視点
スキャンという本来用途で全点が裏返っていた。返り値 normals (N,3)。
手順: `cKDTree で各点の k 近傍(自分自身を含む。k > N` なら N に切り詰め)を取り、
その共分散の最小固有ベクトルを法線にする。返り値 `(N,3)` float64 の単位ベクトル。
`viewpoint` は 3 次元の座標(センサ位置)。点数が 3 未満・近傍が同一直線上だと法線は
不定のまま返る(検証は無い)。`k` が小さいとノイズに弱く、大きいと角が丸まる。
後段: `icp_point2plane の dst_normals、render_shaded 用の法線、normals_to_egi`。
`pointcloud.estimate_normals(台帳 estimate_normals`)と同じ規約。
2-D 進化レジストリへ橋渡しした 3d の op `estimate_point_normals。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。a が k(既定 16)を振る。b` は未使用。
• 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_estimate_point_normals 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op estimate_point_normals. 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).
• fpfh_correspondence — py -3.11 examples_3d/fpfh_correspondence.py
points as input)identity · tb_points_to_voxel · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
typed)tb_points_to_voxel · 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.