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dc.contributor.authorMarin, Dianaen_US
dc.contributor.authorKomon, Patricken_US
dc.contributor.authorOhrhallinger, Stefanen_US
dc.contributor.authorWimmer, Michaelen_US
dc.contributor.editorLiu, Lingjieen_US
dc.contributor.editorAverkiou, Melinosen_US
dc.date.accessioned2024-04-16T15:29:25Z
dc.date.available2024-04-16T15:29:25Z
dc.date.issued2024
dc.identifier.isbn978-3-03868-239-4
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egp.20241037
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egp20241037
dc.description.abstractRecent advancements in scanning technologies and their rise in availability have shifted the focus from reconstructing surfaces from point clouds of small areas to large, e.g., city-wide scenes, containing massive amounts of data. We adapt a surface reconstruction method to work in a distributed fashion on a high-performance cluster, reconstructing datasets with millions of vertices in seconds. We exploit the locality of the connectivity required by the reconstruction algorithm to efficiently divide-andconquer the problem of creating triangulations from very large unstructured point clouds.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 → Point-based models
dc.subjectComputing methodologies → Point
dc.subjectbased models
dc.titleDistributed Surface Reconstructionen_US
dc.description.seriesinformationEurographics 2024 - Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/egp.20241037
dc.identifier.pages2 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