{"id":2204,"date":"2010-12-24T15:30:15","date_gmt":"2010-12-24T15:30:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=2204"},"modified":"2010-12-24T15:30:15","modified_gmt":"2010-12-24T15:30:15","slug":"ray-casting-deformable-models-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2204","title":{"rendered":"Ray Casting Deformable Models on the GPU"},"content":{"rendered":"<p>The GPUs pack high computation power and a restricted architecture into easily available hardware today. They are now used as computation co-processors and come with programming models that treat them as standard parallel architectures. We explore the problem of realtime ray casting of large deformable models (over a million triangles) on large displays (a million pixels) on an off-the-shelf GPU in this paper. Ray casting is an inherently parallel and highly compute intensive operation. We build a GPU-efficient three-dimensional data structure for this purpose and a corresponding algorithm that uses it for fast ray casting. We also present fast methods to build the data structure on the SIMD GPUs, including a fast multi-split operation. We achieve real-time ray-casting of a million triangle model onto a million pixels on current Nvidia GPUs using the CUDA model. Results are presented on the data structure building and ray casting on a number of models. The ideas presented here are likely to extend to later models and architectures of the GPU as well as to other multi-core architectures.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The GPUs pack high computation power and a restricted architecture into easily available hardware today. They are now used as computation co-processors and come with programming models that treat them as standard parallel architectures. We explore the problem of realtime ray casting of large deformable models (over a million triangles) on large displays (a million [&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":[180,11,89,3],"tags":[1797,1782,14,20,183,144],"class_list":["post-2204","post","type-post","status-publish","format-standard","hentry","category-3d-graphics-and-realism","category-computer-science","category-nvidia-cuda","category-paper","tag-3d-graphics-and-realism","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-rendering"],"views":2003,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2204","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=2204"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2204\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}