Yang, JinyuanCampbell, Abraham G.Liu, LingjieAverkiou, Melinos2024-04-302024-04-302024978-3-03868-239-41017-4656https://doi.org/10.2312/egp.20241040https://diglib.eg.org/handle/10.2312/egp20241040In 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → RasterizationComputing methodologies → Rasterization10.2312/egp.202410402 pages