摘要
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 emcient 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.
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 emcient 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.
基金
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
the National Natural Science Foundation of Zhejiang Province of China under Grant No.Y1100069
