Connectivity-Based Segmentation for GPU-Accelerated Mesh Decompression

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...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 27; no. 6; pp. 1110 - 1118
Main Author 赵杰伊 唐敏 童若锋
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.11.2012
Springer Nature B.V
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Subjects
Algorithms
Analysis
Artificial Intelligence
Boundaries
Computer Science
Connectivity
Data compression
Data Structures and Information Theory
Finite element method
GeForce
GPU
Information Systems Applications (incl.Internet)
Mathematical models
NVIDIA
Performance evaluation
R&D
Regular Paper
Research & development
Sampling
Segmentation
Software Engineering
Stores
Studies
Theory of Computation
Three dimensional
减压
分割
压缩算法
网格生成
连接
mesh segmentation
connectivity compression
Edgebreaker
parallel decompression
GPU
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-012-1289-x

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Summary: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.
Bibliography:parallel decompression, mesh segmentation, connectivity compression, GPU, Edgebreaker
11-2296/TP
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.
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-012-1289-x