typed op• Data kinds: points → signal
• Call: fullseye.apply(img, "tb_iss_keypoints", 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_iss_keypoints: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_iss_keypoints: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_iss_keypoints: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_iss_keypoints: other inputs (docs site)
ISS (Intrinsic Shape Signatures, equivalent to a 3D Harris) keypoint detector. Uses the smallest eigenvalue λ3 of the local covariance as the saliency, restricts candidates to points whose eigenvalues are distinct (i.e. well-defined orientation), and sparsely selects them via NMS. Rotation-invariant. Returns an array of point indices.
> The detailed description below is the original text — the summary and the headings are translated.
引数:
• `points (N,3)。radius`: saliency 用の近傍球半径(点群と同じ単位)。
• `nms_radius: 非最大抑制の距離。None なら 0.6*radius`。
• `gamma21・gamma32`: 固有値比 λ2/λ1、λ3/λ2 の上限(既定 0.99)。両方ともこれ
未満の点だけが候補(比が 1 に近い=等方で向きが決まらない点を除く)。
• `max_kp: 返す上限。min_neighbors`: 近傍がこれ未満の点は候補にしない(既定 8)。
手順: 各点で `radius` 内の近傍を集め、注目点を原点とした 2 次モーメント行列
`(qᵀq)/n` の固有値 λ1≥λ2≥λ3 を求める(λ1 が 1e-12 以下なら除外)。λ3 を saliency と
して降順に走査し、採用済みの点から `nms_radius` 未満にあるものを捨てる貪欲 NMS。
返り値は int64 の点インデックス配列(saliency 降順)。候補が無ければ長さ 0。
注意: 近傍は注目点で中心化する(重心ではない)ため、平面上の点でも λ3 は厳密には 0 に
ならない。全点で近傍探索する Python ループなので、数万点を超える雲は
`voxel_grid_downsample で間引いてから使う。shot_descriptor` のキーポイント入力に
直結し、`register_shot は内部で radius` の 0.6 倍・NMS 0.3 倍で呼ぶ。
2-D 進化レジストリへ橋渡しした 3d の op `iss_keypoints。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 gamma21(既定 0.99)、b が gamma32`(既定 0.99)を振る。
• 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_iss_keypoints 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op iss_keypoints. 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).
• feature_register — py -3.11 examples_3d/feature_register.py
signal as input)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_smooth_funct_1d_mean · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d
typed)tb_points_to_voxel · tb_estimate_point_normals · 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.