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dc.contributor.authorYang, Jinyuanen_US
dc.contributor.authorCampbell, Abraham G.en_US
dc.contributor.editorLiu, Lingjieen_US
dc.contributor.editorAverkiou, Melinosen_US
dc.date.accessioned2024-04-16T15:29:26Z
dc.date.available2024-04-16T15:29:26Z
dc.date.issued2024
dc.identifier.isbn978-3-03868-239-4
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egp.20241040
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egp20241040
dc.description.abstractIn this paper, we introduce VirtualVoxelCrowd, which aims to address the challenges of data scale and overdraw in massive crowd rendering applications. The approach leverages multiple levels of detail and multi-pass culling to reduce rendering workload and overdraw. VirtualVoxelCrowd supports rendering of up to one billion characters, achieving unprecedented scale on standard graphics hardware while rendering subpixel-level voxels to prevent the level of detail transition artifacts. This method offers significant improvements in handling massive animated crowd visualization, establishing a new possibility for dynamic, large-scale scene rendering.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 → Rasterization
dc.subjectComputing methodologies → Rasterization
dc.titleVirtualVoxelCrowd: Rendering One Billion Characters at Real-Timeen_US
dc.description.seriesinformationEurographics 2024 - Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/egp.20241040
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