{"id":6083,"date":"2011-10-27T17:32:48","date_gmt":"2011-10-27T14:32:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=6083"},"modified":"2011-10-27T17:32:48","modified_gmt":"2011-10-27T14:32:48","slug":"development-of-a-volume-rendering-system-using-3d-texture-compression-techniques-on-general-purpose-personal-computers","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6083","title":{"rendered":"Development of a volume rendering system using 3D texture compression techniques on general-purpose personal computers"},"content":{"rendered":"<p>In this paper, we present the development of a highspeed volume rendering system that combines 3D texture compression and parallel programming techniques for rendering multiple high-resolution 3D images obtained with medical or industrial CT. The 3D texture compression algorithm (DXT5) provides extremely high efficiency since it reduces the memory consumption to 1\/4 of the original without having a negative impact on the image quality or display speed. By using this approach, it is possible to use personal computers with generalpurpose graphics capabilities to display high-resolution 3D images or groups of multiple 3D images obtained with medical or industrial CT in real time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present the development of a highspeed volume rendering system that combines 3D texture compression and parallel programming techniques for rendering multiple high-resolution 3D images obtained with medical or industrial CT. The 3D texture compression algorithm (DXT5) provides extremely high efficiency since it reduces the memory consumption to 1\/4 of the original [&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":[36,11,3],"tags":[1787,832,1782,20,1111,182,70,144],"class_list":["post-6083","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-compression","tag-computer-science","tag-nvidia","tag-nvidia-quadro-fx-1800","tag-opengl","tag-programming-techniques","tag-rendering"],"views":2334,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6083","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=6083"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6083\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6083"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6083"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6083"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}