{"id":2250,"date":"2010-12-27T13:52:16","date_gmt":"2010-12-27T13:52:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=2250"},"modified":"2010-12-27T13:52:16","modified_gmt":"2010-12-27T13:52:16","slug":"gpu-supported-patch-based-tessellation-for-dual-subdivision","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2250","title":{"rendered":"GPU Supported Patch-Based Tessellation for Dual Subdivision"},"content":{"rendered":"<p>A novel patch-based tessellation method for a dual subdivision scheme, the Doo-Sabin subdivision, is presented. Patch-based refinement for face-split subdivision schemes such as Catmull-Clark subdivision or Loop subdivision has been widely studied. But there is no patch-based tessellation algorithm for dual subdivision scheme yet. The method presented in this paper is the first attempt to fill up that gap. The new method uses an 1D array to hold vertices; it creates a patch corresponding to a vertex in the original mesh and does not have any numerical roundoff gaps on patch boundaries. These characteristics are different from those of patch-based refinements for face-split subdivision schemes. Experimental results show that our algorithm achieves real time tessellation performance for moderate meshes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A novel patch-based tessellation method for a dual subdivision scheme, the Doo-Sabin subdivision, is presented. Patch-based refinement for face-split subdivision schemes such as Catmull-Clark subdivision or Loop subdivision has been widely studied. But there is no patch-based tessellation algorithm for dual subdivision scheme yet. The method presented in this paper is the first attempt to [&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":[11,89,3],"tags":[1782,14,20,226,144],"class_list":["post-2250","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gt","tag-rendering"],"views":1820,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2250","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=2250"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2250\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2250"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2250"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2250"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}