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moving least squares : ウィキペディア英語版
moving least squares
Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
In computer graphics, the moving least squares method is useful for reconstructing a surface from a set of points. Often it is used to create a 3D surface from a point cloud through either downsampling or upsampling.
==Definition==

Consider a function f: \mathbb^n \to \mathbb and a set of sample points S = \ where x_i \in \mathbb^n and the f_i's are real numbers. Then, the moving least square approximation of degree m at the point x is \tilde(x) where \tilde minimizes the weighted least-square error
:\sum_ (p(x_i)-f_i)^2\theta(\|x-x_i\|)
over all polynomials p of degree m in \mathbb^n. \theta(s) is the weight and it tends to zero as s\to \infty.
In the example \theta(s) = e^. The smooth interpolator of "order 3" is a quadratic interpolator.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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