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    Efficient Point Merging Using Data Parallel Techniques

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    Date
    2019
    Author
    Yenpure, Abhishek ORCID
    Childs, Hank ORCID
    Moreland, Kenneth ORCID
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    Abstract
    We study the problem of merging three-dimensional points that are nearby or coincident. We introduce a fast, efficient approach that uses data parallel techniques for execution in various shared-memory environments. Our technique incorporates a heuristic for efficiently clustering spatially close points together, which is one reason our method performs well against other methods. We then compare our approach against methods of a widely-used scientific visualization library accompanied by a performance study that shows our approach works well with different kinds of parallel hardware (many-core CPUs and NVIDIA GPUs) and data sets of various sizes.
    BibTeX
    @inproceedings {v.20191112,
    booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
    editor = {Childs, Hank and Frey, Steffen},
    title = {{Efficient Point Merging Using Data Parallel Techniques}},
    author = {Yenpure, Abhishek and Childs, Hank and Moreland, Kenneth},
    year = {2019},
    publisher = {The Eurographics Association},
    ISSN = {1727-348X},
    ISBN = {978-3-03868-079-6},
    DOI = {10.2312/pgv.20191112}
    }
    URI
    https://doi.org/10.2312/pgv.20191112
    https://diglib.eg.org:443/handle/10.2312/pgv20191112
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