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CPU-GPU Algorithms for Triangular Surface Mesh Simplification

Shontz, Suzanne M. and Dragos M. Nistor

21st International Meshing Roundtable, Springer-Verlag, pp.475-492, October 7-10 2012

IMR
PROCEEDINGS

21st International Meshing Roundtable
San Jose, CA
October 7-10,2012

Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS, U.S.A.
Email: sshontz@math.msstate.edu

Summary
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 na®.ve 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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