This paper presents data-parallel algorithms for surface and solid voxelization on graphics hardware. First, a novel conservative surface voxelization technique, setting all voxels overlapped by a mesh’s triangles, is introduced, which is up to one order of magnitude faster than previous solutions leveraging the standard rasterization pipeline. We then show how the involved new triangle\/box overlap test can be adapted to yield a 6-separating surface voxelization, which is thinner but still connected and gap-free. Complementing these algorithms, both a triangle-parallel and a tile-based technique for solid voxelization are subsequently presented. Finally, addressing the high memory consumption of high-resolution voxel grids, we introduce a novel octree-based sparse solid voxelization approach, where only close to the solid’s boundary finest-level voxels are stored, whereas uniform interior and exterior regions are represented by coarser-level voxels. This representation is created directly from a mesh without requiring a full intermediate solid voxelization, enabling GPU-based voxelizations of unprecedented size.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents data-parallel algorithms for surface and solid voxelization on graphics hardware. First, a novel conservative surface voxelization technique, setting all voxels overlapped by a mesh’s triangles, is introduced, which is up to one order of magnitude faster than previous solutions leveraging the standard rasterization pipeline. We then show how the involved new triangle\/box […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[180,36,11,89,3],"tags":[1797,1787,1782,14,20,251,379],"class_list":["post-5264","post","type-post","status-publish","format-standard","hentry","category-3d-graphics-and-realism","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-3d-graphics-and-realism","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-geforce-gtx-480"],"views":2920,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5264","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5264"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5264\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5264"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5264"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}