{"id":3336,"date":"2011-03-24T21:00:32","date_gmt":"2011-03-24T21:00:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=3336"},"modified":"2011-03-24T21:00:32","modified_gmt":"2011-03-24T21:00:32","slug":"optimizing-gpu-volume-rendering","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3336","title":{"rendered":"Optimizing GPU Volume Rendering"},"content":{"rendered":"<p>Volume Rendering methods employing the GPU capabilities offer high performance on off-the-shelf hardware. In this article, we discuss the various bottlenecks found in the graphics hardware when performing GPU-based Volume Rendering. The specific properties of each bottleneck and the trade-offs between them are described. Further we present a novel strategy to balance the load on the identified bottlenecks, without compromising the image quality. Our strategy introduces a two-staged space-skipping, whereby the first stage applies bricking on a semi-regular grid, and the second stage uses octrees to reach a finer granularity. Additionally we apply early ray termination to the bricks. We demonstrate how the two stages address the individual bottlenecks, and how they can be tuned for a specific hardware pipeline. The described method takes into account that the rendered volume may exceed the available texture memory. Our approach further allows fast run-time changes of the transfer function. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Volume Rendering methods employing the GPU capabilities offer high performance on off-the-shelf hardware. In this article, we discuss the various bottlenecks found in the graphics hardware when performing GPU-based Volume Rendering. The specific properties of each bottleneck and the trade-offs between them are described. Further we present a novel strategy to balance the load on [&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":[7,1028,1782,20,420,182,144,134],"class_list":["post-3336","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-ati","tag-ati-firegl-x1","tag-computer-science","tag-nvidia","tag-nvidia-quadro-fx-3400","tag-opengl","tag-rendering","tag-visualization"],"views":2272,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3336","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=3336"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3336\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3336"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3336"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3336"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}