Adaptive Surface Reconstruction from Non Uniform Point Sets

Abstract

In this paper, a proposed algorithm for surface reconstruction from uniform or non-uniform point sets is introduced. The points are typically acquired with multiple range scans of any 3D object. The proposed algorithm follows the advancing front paradigm to build the reconstructed surface employing a variable radius moving ball that expands and shrinks continuously based on the sampling density. Starting with a user-specified initial radius, this initial ball may touch three points without containing any other point forming a seed triangle. For any edge, another point is found to form a ball with minimum radius generating another triangle. The process continues until generating all possible edges. The algorithm is theoretically proved under certain sampling criteria on the input data set. The proposed algorithm was applied on different datasets and compared favorably with the most eminent techniques. The key issues for comparisons were the reconstructed surface quality, the memory usage and the execution time. The present algorithm bested others in treating non uniform samples, samples with sharp edges and samples with small holes.