typed op• Data kinds: points → points
• Call: fullseye.apply(img, "tb_estimate_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_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_normals: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_estimate_normals: other inputs (docs site)
Point-cloud normals unified to point outward (away from the local centroid of neighbors). → (N,3).
> The detailed description below is the original text — the summary and the headings are translated.
手順(各点、Python ループ): `cKDTree で自身を含む k+1 近傍(k は N-1 に切り詰め)を取り、クエリ点を原点にした近傍座標の散布行列 local.T @ local` の最小固有ベクトルを法線にする(単位長)。向きは「近傍重心との内積が正なら反転」= 近傍重心から離れる側に揃える。
• 近傍が 5 点未満の点は固定値 `(0, 0, 1)` を返す(推定していない)。
• 向き付けは局所ヒューリスティクスで、閉じた凸形状なら外向きだが、開いた面・薄板・凹部では隣接点どうしで向きが食い違い得る(大域一貫性は保証しない)。大域的に揃えるには `orient_normals に通すか、最初から estimate_oriented_normals を使う。organized 深度画像なら normals_from_depth` が視点向きで速い。
• 返り値は float64 (N,3)。`k` 既定 25。決定論的。
• 内部は `principal_curvatures` と同じ計算を通る(法線推定にも二次曲面フィットまで走る)ので、点数が多いと遅い。
2-D 進化レジストリへ橋渡しした 3d の op `estimate_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 25)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_normals 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op estimate_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).
• cylinder_axis_metrology — py -3.11 examples_3d/cylinder_axis_metrology.py
• feature_register — py -3.11 examples_3d/feature_register.py
• oriented_normals — py -3.11 examples_3d/oriented_normals.py
points as input)identity · 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
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.