Efficient Cone Singularity Construction for Conformal Parameterizations

Abstract

We propose an efficient method to construct sparse cone singularities under distortion-bounded constraints for conformal parameterizations. Central to our algorithm is using the technique of shape derivatives to move cones for distortion reduction without changing the number of cones. In particular, the supernodal sparse Cholesky update significantly accelerates this movement process. To satisfy the distortion-bounded constraint, we alternately move cones and add cones. The capability and feasibility of our approach are demonstrated over a data set containing 3885 models. Compared with the state-of-the-art method, we achieve an average acceleration of 15 times and slightly fewer cones for the same amount of distortion.