{"id":8550,"date":"2012-11-24T23:51:53","date_gmt":"2012-11-24T21:51:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=8550"},"modified":"2012-11-24T23:51:53","modified_gmt":"2012-11-24T21:51:53","slug":"gpu-isosurface-raycasting-of-fcc-datasets","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8550","title":{"rendered":"GPU Isosurface Raycasting of FCC Datasets"},"content":{"rendered":"<p>This paper presents an efficient and accurate isosurface rendering algorithm for the natural C^1 splines on the face-centered cubic (FCC) lattice. Leveraging fast and accurate evaluation of a spline field and its gradient, accompanied by efficient empty-space skipping, the approach generates high-quality isosurfaces of FCC datasets at interactive speed (20-70 fps). The pre-processing computation (quasi-interpolation and min\/max cell construction) is improved 20 to 30-fold by OpenCL kernels. In addition, a novel indexing scheme is proposed that allows an FCC dataset to be stored as a four-channel 3D texture. When compared with other reconstruction schemes on the Cartesian and BCC (body-centered cubic) lattices, this method can be considered a practical reconstruction scheme that offers both quality and performance. The OpenCL and GLSL (OpenGL Shading Language) source codes are provided as a reference.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an efficient and accurate isosurface rendering algorithm for the natural C^1 splines on the face-centered cubic (FCC) lattice. Leveraging fast and accurate evaluation of a spline field and its gradient, accompanied by efficient empty-space skipping, the approach generates high-quality isosurfaces of FCC datasets at interactive speed (20-70 fps). The pre-processing computation (quasi-interpolation [&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":[36,11,90,3],"tags":[1787,1782,187,1015,1793,182,144],"class_list":["post-8550","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-glsl","tag-nvidia-geforce-gtx-460","tag-opencl","tag-opengl","tag-rendering"],"views":2148,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8550","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=8550"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8550\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8550"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8550"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8550"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}