Extracting Boundary Surface of Arbitrary Topology from Volumetric Datasets

Abstract

This paper presents a novel, powerful reconstruction algorithm that can recover correct shape geometry as well as its unknown topology from arbitrarily complicated volumetric datasets. The algorithm starts from a simple seed model (of genus zero) that can be initialized automatically without user intervention. The deformable behavior of the model is then governed by a locally defined objective function associated with each vertex of the model. Through the numerical computation of function optimization, the algorithm can adaptively subdivide the model geometry, automatically detect self-collision of the model, properly modify its topology (because of the occurrence of self-collision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global subdivision.