Parallel computation of spherical parameterizations for mesh analysis

Mesh parameterization is central to a broad spectrum of applications. In this paper, we present a novel approach to spherical mesh parameterization based on an iterative quadratic solver that is efficiently parallelizable on modern massively parallel architectures. We present an extensive analysis of performance results on both GPU and multicore architectures. We introduce a number of heuristics that exploit various system characteristics of the underlying architectures to speed up the parallel realization of our algorithms. Furthermore, we demonstrate the applicability of our approach to real-time feature detection, mesh decomposition and similarity-based 3D object retrieval. Finally, we offer visual results and a demonstration video.

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► Introduces a novel iterative quadratic solver for spherical mesh parameterization.
► Presents a very efficient parallel implementation along with a number of heuristics that speed up significantly the parallel realization on modern hardware.
► Demonstrates the usefulness of the parallel mesh parameterization algorithm in several applications that exploit mesh morphology analysis.