New Solutions for Surface Reconstruction from Discrete Point Data by Means of Computational Intelligence

Abstract

Surface reconstruction by means of triangulation of digitized point data leads to computational complex optimization problems. Here, deterministic algorithms often result in insufficient solutions or very long computation times. In this article, alternative methods of computational intelligence are discussed. A comparative analysis of two evolutionary algorithms applied to four different smoothness criteria for the triangulation of sparse point data sets is presented. optimally triangulated surfaces are the basis for many practical applications. The results presented here cover the efficient implementation and the influence of different triangulations for an adequate touch probe radius compensation (TPRC).