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Connectivity-Based Segmentation for GPU-Accelerated Mesh Decompression

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Abstract

We present a novel algorithm to partition large 3D meshes for GPU-accelerated decompression. Our formulation focuses on minimizing the replicated vertices between patches, and balancing the numbers of faces of patches for efficient parallel computing. First we generate a topology model of the original mesh and remove vertex positions. Then we assign the centers of patches using geodesic farthest point sampling and cluster the faces according to the geodesic distance to the centers. After the segmentation we swap boundary faces to fix jagged boundaries and store the boundary vertices for whole-mesh preservation. The decompression of each patch runs on a thread of GPU, and we evaluate its performance on various large benchmarks. In practice, the GPU-based decompression algorithm runs more than 48x faster on NVIDIA GeForce GTX 580 GPU compared with that on the CPU using single core.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Jie-Yi Zhao, Min Tang & Ruo-Feng Tong (Member, CCF)

Authors
  1. Jie-Yi Zhao
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  2. Min Tang
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  3. Ruo-Feng Tong
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Corresponding author

Correspondence to Min Tang.

Additional information

The work is supported in part by the National Basic Research 973 Program of China under Grant No. 2011CB302205, the National High Technology Research and Development 863 Program of China under Grant No. 2012BAD35B01, the National Natural Science Foundation of China under Grant No. 61170140, and the National Natural Science Foundation of Zhejiang Province of China under Grant No. Y1100069.

**The preliminary version of the paper was published in the Proceedings of the 2012 Computational Visual Media Conference.

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Zhao, JY., Tang, M. & Tong, RF. Connectivity-Based Segmentation for GPU-Accelerated Mesh Decompression. J. Comput. Sci. Technol. 27, 1110–1118 (2012). https://doi.org/10.1007/s11390-012-1289-x

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  • DOI: https://doi.org/10.1007/s11390-012-1289-x

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