{"id":4851,"date":"2011-07-22T18:28:26","date_gmt":"2011-07-22T15:28:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=4851"},"modified":"2011-07-22T18:28:26","modified_gmt":"2011-07-22T15:28:26","slug":"real-time-3d-video-synthesis-from-binocular-capture-system-based-on-commodity-graphic-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4851","title":{"rendered":"Real-time 3D video synthesis from binocular capture system based on commodity graphic hardware"},"content":{"rendered":"<p>In this paper, a real-time 3D video synthesis method suitable for implementation on commodity graphic hardware is presented. The system consists of pre-calibrated binocular stereo cameras and an NVIDIA GeForce 8 Series graphic card. Recently, most research has focused on improving the quality of depth maps, which is usually time-consuming and unsuitable for real-time reconstruction. In our method, we combine a plane-sweeping algorithm with view synthesis, and thus the new views between the captured images can be real-time generated without generating the intermediate depth map. The Programmable Fragment Shader technology is used to accelerate the process of Sum-of-Square-Difference (SSD) dissimilarity measure of the correlation pixels and the final color calculation. Experimental results show that our method can efficiently implement the real-time 3D view synthesis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a real-time 3D video synthesis method suitable for implementation on commodity graphic hardware is presented. The system consists of pre-calibrated binocular stereo cameras and an NVIDIA GeForce 8 Series graphic card. Recently, most research has focused on improving the quality of depth maps, which is usually time-consuming and unsuitable for real-time reconstruction. [&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":[36,33,3],"tags":[1787,1786,20,297],"class_list":["post-4851","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-image-processing","category-paper","tag-algorithms","tag-image-processing","tag-nvidia","tag-real-time-graphics"],"views":2469,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4851","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=4851"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4851\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}