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Real-time Mesh Simplification Using the GPU Christopher DeCoro, Natalya Tatarchuk
Abstract Recent advances in real-time rendering have allowed the GPU implementation of traditionally CPU-restricted algorithms, often with performance increases of an order of magnitude or greater. Such gains are achieved by leveraging the large-scale parallelism of the GPU towards applications that are well-suited for these streaming architectures. By contrast, mesh simplification has traditionally been viewed as a non-interactive process not readily amenable to GPU acceleration. We demonstrate how it becomes practical for real-time use through our method, and that the use of the GPU even for offline simplification leads to significant increases in performance. Our approach for mesh decimation adopts a vertex-clustering method to the GPU by taking advantage of a new addition to the rendering pipeline - the \textit{geometry shader} stage. We present a novel general-purpose data structure designed for streaming architectures called the \emph{probabilistic octree}, which allows for much of the flexibility of offline implementations, including sparse encoding and variable level-of-detail. We demonstrate successful use of this data structure in our GPU implementation of mesh simplification. We can generate adaptive levels of detail by applying non-linear warping functions to the cluster map in order to improve resulting simplification quality. Our GPU-accelerated approach enables simultaneous construction of multiple levels of detail and out-of-core simplification of extremely large polygonal meshes. Citation (BibTeX) Christopher DeCoro and Natalya Tatarchuk. Paper Slides |