Feature Preserving Filling of Holes on Point Sampled Surfaces Based on Tensor Voting

Abstract

This paper presents a novel hole filling method for the scattered point sampled surfaces, particularly for recovering the missing points at featured curves or corners. Firstly, a tensor voting based multicriterion is proposed to identify the hole boundary points; accordingly, the holes on point sampled surface are classified into featured holes and nonfeatured holes. Secondly, a novel spline curve guided tensor voting mechanism is proposed and used in inference of missing feature points. Thirdly, the featured holes are split into nonfeatured holes using local projection. Then, a plane guided tensor voting mechanism is proposed to recover the missing surface points. Experimental results validate the effectiveness and accuracy of proposed methods in filling holes on point sampled surface including the sharp features.