{"id":2072,"date":"2010-12-14T15:56:42","date_gmt":"2010-12-14T15:56:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=2072"},"modified":"2010-12-14T15:56:42","modified_gmt":"2010-12-14T15:56:42","slug":"fast-isosurface-rendering-on-a-gpu-by-cell-rasterization-2","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2072","title":{"rendered":"Fast Isosurface Rendering on a GPU by Cell Rasterization"},"content":{"rendered":"<p>This paper presents a fast, high-quality, GPU-based isosurface rendering pipeline for implicit surfaces defined by a regular volumetric grid. GPUs are designed primarily for use with polygonal primitives, rather than volume primitives, but here we directly treat each volume cell as a single rendering primitive by designing a vertex program and fragment program on a commodity GPU. Compared with previous raycasting methods, ours has a more effective memory footprint (cache locality) and better coherence between multiple parallel SIMD processors. Furthermore, we extend and speed up our approach by introducing a new view-dependent sorting algorithm to take advantage of the early-z-culling feature of the GPU to gain significant performance speed-up. As another advantage, this sorting algorithm makes multiple transparent isosurfaces rendering available almost for free. Finally, we demonstrate the effectiveness and quality of our techniques in several real-time rendering scenarios and include analysis and comparisons with previous work.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a fast, high-quality, GPU-based isosurface rendering pipeline for implicit surfaces defined by a regular volumetric grid. GPUs are designed primarily for use with polygonal primitives, rather than volume primitives, but here we directly treat each volume cell as a single rendering primitive by designing a vertex program and fragment program on a [&hellip;]<\/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":[11,3],"tags":[1782,333,144],"class_list":["post-2072","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-image-generation","tag-rendering"],"views":1657,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2072","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=2072"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2072\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2072"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2072"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2072"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}