{"id":5841,"date":"2011-10-09T13:30:37","date_gmt":"2011-10-09T10:30:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=5841"},"modified":"2011-10-09T13:30:37","modified_gmt":"2011-10-09T10:30:37","slug":"parallel-and-efficient-boolean-on-polygonal-solids","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5841","title":{"rendered":"Parallel and efficient Boolean on polygonal solids"},"content":{"rendered":"<p>We present a novel framework which can efficiently evaluate approximate Boolean set operations for B-rep models by highly parallel algorithms. This is achieved by taking axis-aligned surfels of Layered Depth Images (LDI) as a bridge and performing Boolean operations on the structured points. As compared with prior surfel-based approaches, this paper has much improvement. Firstly, we adopt key-data pairs to store LDI more compactly. Secondly, robust depth peeling is investigated to overcome the bottleneck of layer-complexity. Thirdly, an out-of-core tiling technique is presented to overcome the limitation of memory. Real-time feedback is provided by streaming the proposed pipeline on the many-core graphics hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a novel framework which can efficiently evaluate approximate Boolean set operations for B-rep models by highly parallel algorithms. This is achieved by taking axis-aligned surfels of Layered Depth Images (LDI) as a bridge and performing Boolean operations on the structured points. As compared with prior surfel-based approaches, this paper has much improvement. Firstly, [&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":[11,89,33,3],"tags":[1782,14,1786,379],"class_list":["post-5841","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-image-processing","category-paper","tag-computer-science","tag-cuda","tag-image-processing","tag-nvidia-geforce-gtx-480"],"views":1758,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5841","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=5841"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5841\/revisions"}],"predecessor-version":[{"id":5842,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5841\/revisions\/5842"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5841"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5841"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}