# 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 [math]\displaystyle{ f: \mathbb{R}^n \to \mathbb{R} }[/math] and a set of sample points [math]\displaystyle{ S = \{ (x_i,f_i) | f(x_i) = f_i \} }[/math]. Then, the moving least square approximation of degree [math]\displaystyle{ m }[/math] at the point [math]\displaystyle{ x }[/math] is [math]\displaystyle{ \tilde{p}(x) }[/math] where [math]\displaystyle{ \tilde{p} }[/math] minimizes the weighted least-square error

- [math]\displaystyle{ \sum_{i \in I} (p(x_i)-f_i)^2\theta(\|x-x_i\|) }[/math]

over all polynomials [math]\displaystyle{ p }[/math] of degree [math]\displaystyle{ m }[/math] in [math]\displaystyle{ \mathbb{R}^n }[/math]. [math]\displaystyle{ \theta(s) }[/math] is the weight and it tends to zero as [math]\displaystyle{ s\to \infty }[/math].

In the example [math]\displaystyle{ \theta(s) = e^{-s^2} }[/math]. The smooth interpolator of "order 3" is a quadratic interpolator.

## See also

## References

- The approximation power of moving least squares David Levin, Mathematics of Computation, Volume 67, 1517-1531, 1998 [1]
- Moving least squares response surface approximation: Formulation and metal forming applications Piotr Breitkopf; Hakim Naceur; Alain Rassineux; Pierre Villon, Computers and Structures, Volume 83, 17-18, 2005.
- Generalizing the finite element method: diffuse approximation and diffuse elements, B Nayroles, G Touzot. Pierre Villon, P, Computational Mechanics Volume 10, pp 307-318, 1992

## External links

Original source: https://en.wikipedia.org/wiki/ Moving least squares.
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