Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

被引：13

作者：

Crivellaro, Alberto ^{[1
]}

Perotto, Simona ^{[2
]}

Zonca, Stefano ^{[2
]}

机构：

[1] Ecole Polytech Fed Lausanne, CVLab, Off BC 306,Stn 14, CH-1015 Lausanne, Switzerland

[2] Politecn Milan, Dipartimento Matemat, MOX, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy

来源：

APPLIED NUMERICAL MATHEMATICS | 2017年 / 113卷

关键词：

Multivariate interpolation; Least-squares approximation; Adaptive algorithm; Radial basis functions; 3D noisy and lacking scattered data; SURFACE RECONSTRUCTION; INTERPOLATION; PARTITION; UNITY;

D O I：

10.1016/j.apnum.2016.11.003

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We propose new algorithms to overcome two of the most constraining limitations of surface reconstruction methods in use. In particular, we focus on the large amount of data characterizing standard acquisitions by scanner and the noise intrinsically introduced by measurements. The first algorithm represents an adaptive multi-level interpolating approach, based on an implicit surface representation via radial basis functions. The second algorithm is based on a least-squares approximation to filter noisy data. The third approach combines the two algorithms to merge the correspondent improvements. An extensive numerical validation is performed to check the performances of the proposed techniques. (C) 2016 IMACS. Published by Elsevier B.V. All rights reserved.

引用

页码：93 / 108

页数：16

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