Study of parallel techniques applied to surface reconstruction from unorganized and unoriented point clouds

Abstract

Nowadays, digital representations of real objects are becoming biggeras scanning processes are more accurate, so the time required for thereconstruction of the scanned models is also increasing.This thesis studies the application of parallel techniques in the surfacereconstruction problem, in order to improve the processing time required to obtain the final mesh. It is shown how local interpolating triangulationsare suitable for global reconstruction, at the time that it is possible totake advantage of the independent nature of these triangulations to design highly efficient parallel methods.A parallel surface reconstruction method is presented, based on local Delaunay triangulations. The input points do not present any additionalinformation, such as normals, nor any known structure. This method hasbeen designed to be GPU friendly, and two implementations are presented.To deal the inherent problems related to interpolating techniques (suchas noise, outliers and non-uniform distribution of points), a consolidationprocess is studied and a parallel points projection operator is presented, aswell as its implementation in the GPU. This operator is combined with thelocal triangulation method to obtain a better reconstruction.This work also studies the possibility of using dynamic reconstructiontechniques in a parallel fashion. The method proposed looks for a betterinterpretation and recovery of the shape and topology of the target model.