{"id":11146,"date":"2013-12-25T23:38:01","date_gmt":"2013-12-25T21:38:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=11146"},"modified":"2013-12-25T23:38:01","modified_gmt":"2013-12-25T21:38:01","slug":"a-convex-relaxation-approach-to-space-time-multi-view-3d-reconstruction","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11146","title":{"rendered":"A Convex Relaxation Approach to Space Time Multi-view 3D Reconstruction"},"content":{"rendered":"<p>We propose a convex relaxation approach to space-time 3D reconstruction from multiple videos. Generalizing the works [16], [8] to the 4D setting, we cast the problem of reconstruction over time as a binary labeling problem in a 4D space. We propose a variational formulation which combines a photoconsistency based data term with a spatio-temporal total variation regularization. In particular, we propose a novel data term that is both faster to compute and better suited for wide-baseline camera setups when photoconsistency measures are unreliable or missing. The proposed functional can be globally minimized using convex relaxation techniques. Numerous experiments on a variety of publically available data sets demonstrate that we can compute detailed and temporally consistent reconstructions. In particular, the temporal regularization allows to reduce jittering of voxels over time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a convex relaxation approach to space-time 3D reconstruction from multiple videos. Generalizing the works [16], [8] to the 4D setting, we cast the problem of reconstruction over time as a binary labeling problem in a 4D space. We propose a variational formulation which combines a photoconsistency based data term with a spatio-temporal total [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,73,89,3],"tags":[1782,1791,14,20,1006],"class_list":["post-11146","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-tesla-c2070"],"views":2233,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11146","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=11146"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11146\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11146"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11146"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}