In this paper, we propose a real-time multi-view depth generation method using compute unified device architecture (CUDA) multi-graphics processing units (GPU). The objective is to generate multi-view depth maps in real-time. We employ eight color cameras and three depth cameras. After capturing multi-view color and depth data, we warp the depth information to color camera positions. Then joint bilateral filtering (JBF) is performed to fill empty regions. Such a procedure is accelerated by CUDA which is one of general-purpose computing on GPU (GPGPU). As a result, depth maps of eight views are generated at a rate of 23 frames per second (fps) on a single GPU computer. When using a multi-GPU computer, depth generation at 34 fps was achieved.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a real-time multi-view depth generation method using compute unified device architecture (CUDA) multi-graphics processing units (GPU). The objective is to generate multi-view depth maps in real-time. We employ eight color cameras and three depth cameras. After capturing multi-view color and depth data, we warp the depth information to color camera […]<\/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":[89,33,3],"tags":[14,841,1786,20,1470],"class_list":["post-12890","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-filtering","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-titan"],"views":2266,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12890","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=12890"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12890\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}