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

Crivellaro, AlbertoPerotto, SimonaZonca, Stefano2017-03-272017-03-272017-03-27201710.1016/j.apnum.2016.11.003https://infoscience.epfl.ch/handle/20.500.14299/135852WOS:000392786600006We 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.Multivariate interpolationLeast-squares approximationAdaptive algorithmRadial basis functions3D noisy and lacking scattered dataReconstruction of 3D scattered data via radial basis functions by efficient and robust techniquestext::journal::journal article::research article