Extracting Boundary Surface of Arbitrary Topology from Volumetric Datasets

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 selfcollision), continuously evolve the model towards the object boundary, and reduce fitting error and improve fitting quality via global subdivision. Commonly used mesh optimization techniques are employed throughout the geometric deformation and topological variation in order to ensure the model both locally smooth and globally well-defined. We have applied our algorithm to various real and synthetic volumetric datasets in order to empirically verify and validate its utility and efficacy. Our experiments have demonstrated that the new modeling algorithm is extremely valuable for surface reconstruction in volume graphics, volume segmentation in medical imaging, and isosurface extraction in visualization.