tb_mls_smooth — 2D `typed` op

• Data kinds: points → points

• Call: fullseye.apply(img, "tb_mls_smooth", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

tb_mls_smooth: input → output

*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_mls_smooth: 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_mls_smooth: stages (docs site)

On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):

▸ tb_mls_smooth: other inputs (docs site)

Usage

Projects each point onto a local polynomial surface to remove noise (Moving Least Squares smoothing).

> The detailed description below is the original text — the summary and the headings are translated.

点ごとに半径 `radius` 内の近傍を集め、重み付き PCA で局所平面(法線 n と接平面の

2 軸)を推定し、近傍の「接平面上座標 (u,v) → 法線方向の高さ h」に order 次の

多項式曲面をガウス重み付き最小二乗で当てはめる。注目点自身は局所座標の原点 (0,0)

にあたるので、当てはめた曲面の (0,0) での高さ(= 定数項)ぶんだけ法線方向へ動かして

曲面上へ射影する。面の形は保ったままセンサノイズだけを均せる。

Parameters

----------

points : array_like, shape (N, 3)

入力点群。

radius : float

近傍球の半径(> 0)。局所曲面のサポート。

order : int

局所多項式の次数(既定 2)。項数は (order+1)(order+2)/2。

Returns

-------

ndarray, shape (N, 3)

平滑後の点群(順序・点数は保持)。近傍が多項式の項数に満たない点は原位置のまま。

Notes

-----

近似手法である。近傍数が項数未満/局所平面が縮退する点は動かさず原位置を維持する

(穴や境界で暴れないための安全策)。`radius <= 0` は ValueError、空入力は空を返す。

2-D 進化レジストリへ橋渡しした 3d の op `mls_smooth。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 order(既定 2)を振る。b` は未使用。

References (sample data, literature)

• 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.

Try it in Studio

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_mls_smooth 0.50 0.50

▸ Load this pipeline · Load & run

Runnable examples (verified samples that actually call this op)

The examples below call the underlying ledger op mls_smooth. 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).

• pcl_geodesic — py -3.11 examples_3d/pcl_geodesic.py

Ops the type connects to (they accept `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

Same category (`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).*

tb_mls_smooth — 2D typed op