Meshfree Thinning of 3D Point Clouds

An efficient data reduction scheme for the simplification of a surface given by a large set X of 3D point-samples is proposed. The data reduction relies on a recursive point removal algorithm, termed thinning, which outputs a data hierarchy of point-samples for multiresolution surface approximation. The thinning algorithm works with a point removal criterion, which measures the significances of the points in their local neighbourhoods, and which removes a least significant point at each step. For any point x in the current point set Y X, its significance reflects the approximation quality of a local surface reconstructed from neighbouring points in Y . The local surface reconstruction is done over an estimated tangent plane at x by using radial basis functions. The approximation quality of the surface reconstruction around x is measured by using its maximal deviation from the given point-samples X in a local neighbourhood of x. The resulting thinning algorithm is meshfree, i.e., its ...

• Type: Journal
• Year: 2008
• Where: FOCM
• Authors: Nira Dyn, Armin Iske, Holger Wendland
• Keywords: FOCM 2008, Point Removal, Point Removal Algorithm, Thinning