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CPU-GPU Algorithms for Triangular Surface Mesh Simplification

Suzanne M. Shontz, Dragos M. Nistor
Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, U.S.A.
International Meshing Roundtable, 2012

@article{shontz2012cpu,

   title={CPU-GPU Algorithms for Triangular Surface Mesh Simplification},

   author={Shontz, Suzanne M. and Nistor, Dragos M.},

   year={2012}

}

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Mesh simplification and mesh compression are important processes in computer graphics and scientific computing, as such contexts allow for a mesh which takes up less memory than the original mesh. Current simplification and compression algorithms do not take advantage of both the central processing unit (CPU) and the graphics processing unit (GPU). We propose three simplification algorithms, one of which runs on the CPU and two of which run on the GPU. We combine these algorithms into two CPU-GPU algorithms for mesh simplification. Our CPU-GPU algorithms are the naive marking algorithm and the inverse reduction algorithm. Experimental results show that when the algorithms take advantage of both the CPU and the GPU, there is a decrease in running time for simplification compared to performing all of the computation on the CPU. The marking algorithm provides higher simplification rates than the inverse reduction algorithm, whereas the inverse reduction algorithm has a lower running time than the marking algorithm.
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