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dc.contributor.authorWang, Kechunen_US
dc.contributor.authorChen, Renjieen_US
dc.contributor.editorBabaei, Vahiden_US
dc.contributor.editorSkouras, Melinaen_US
dc.date.accessioned2023-05-03T06:03:01Z
dc.date.available2023-05-03T06:03:01Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-209-7
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egs.20231012
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20231012
dc.description.abstractSubdivision surface is a popular technique for geometric modeling. Recently, several parallel implementations have been developed for Loop subdivision on the GPU. However, these methods are built on complex data structures which complicate the implementation and affect the performance, especially on the GPU. In this work, we propose to simply use the sparse adjacency matrix which enables us to implement the Loop subdivision scheme in the most straightforward manner. Our implementation run entirely on the GPU and achieves high performance in runtime with significantly lower memory consumption than the state-of-the-art. Through extensive experiments and comparisons, we demonstrate the efficacy and efficiency of our method.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Computer graphics; Mesh models
dc.subjectComputing methodologies → Computer graphics
dc.subjectMesh models
dc.titleParallel Loop Subdivision with Sparse Adjacency Matrixen_US
dc.description.seriesinformationEurographics 2023 - Short Papers
dc.description.sectionheadersSubdivision and SDFs
dc.identifier.doi10.2312/egs.20231012
dc.identifier.pages49-52
dc.identifier.pages4 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License