Nearest neighbor searches on the GPU: A massively parallel approach for dynamic point clouds

Leite P.; Teixeira J.M.; Farias T.Reis B.Teichrieb V.Kelner J.

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Nearest neighbor searches on the GPU: A massively parallel approach for dynamic point clouds

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摘要

We introduce a GPU grid-based data structure for massively parallel nearest neighbor searches for dynamic point clouds. The implementation provides real-time performance and it is executed on GPU, both grid construction and nearest neighbors (approximate or exact) searches. This minimizes the memory transfer between device and system memories, improving overall performance. The proposed algorithm may be used across different applications with static and dynamic scenarios. Moreover, our data structure supports three-dimensional point clouds and given its dynamic nature, the user can change the data structure's parameters at runtime. The same applies to the number of neighbors to be found. Performance comparisons were made against previous works, endorsing the benefits of our solution. Finally, we were able to develop a real-time Point-Based Rendering application for validation of the data structure. Its drawbacks and data distribution's impact on performance were analysed and some directions for further investigation are given.

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来源
《International Journal of Parallel Programming》 |2012年第3期|313-330|共18页
作者
Leite P.; Teixeira J.M.; Farias T.Reis B.Teichrieb V.Kelner J.;
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作者单位

Computer Science Center, Federal University of Pernambuco, Av. Professor Moraes Rego S/N, Pernambuco, CEP 50670-901, Brazil;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
ANN; KNN; Massive parallel programming; Nearest neighbor query; Point-Based Rendering;

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Nearest neighbor searches on the GPU: A massively parallel approach for dynamic point clouds

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