{"id":1748,"date":"2010-11-29T15:03:25","date_gmt":"2010-11-29T15:03:25","guid":{"rendered":"http:\/\/hgpu.org\/?p=1748"},"modified":"2010-11-29T15:03:25","modified_gmt":"2010-11-29T15:03:25","slug":"detection-of-collisions-and-self-collisions-using-image-space-techniques","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1748","title":{"rendered":"Detection of collisions and self-collisions using image-space techniques"},"content":{"rendered":"<p>Image-space techniques have shown to be very efficient for collision detection in dynamic simulation and animation environments. This paper proposes a new image-space technique for efficient collision detection of arbitrarily shaped, water-tight objects. In contrast to existing approaches that do not consider self-collisions, our approach combines the image-space object representation with information on face orientation to overcome this limitation. While image-space techniques are commonly implemented on graphics hardware, software solutions have been neglected so far. In this paper, the performance of two GPU-based implementations and one CPU-based implementation of the proposed collision detection algorithm are compared. Results suggest, that graphics hardware accelerates collision detection in geometrically complex environments, while the CPU-based implementation provides more flexibility and better performance in case of small environments.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Image-space techniques have shown to be very efficient for collision detection in dynamic simulation and animation environments. This paper proposes a new image-space technique for efficient collision detection of arbitrarily shaped, water-tight objects. In contrast to existing approaches that do not consider self-collisions, our approach combines the image-space object representation with information on face orientation [&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":[11,3],"tags":[137,1782,20,414,182],"class_list":["post-1748","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-collision-detection","tag-computer-science","tag-nvidia","tag-nvidia-geforce-fx-5800-ultra","tag-opengl"],"views":2121,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1748","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=1748"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1748\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1748"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1748"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1748"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}