In this paper, we propose a local cost aggregation approach for real time stereo vision on a graphics processing unit (GPU). Recent research shows that local approaches based on carefully designed cost aggregation strategies can outperform many global approaches. Among those local aggregation approaches, adaptive-weight window produces the best quality disparity map under real-time constraint, but it is slower than other local approaches. We propose a very fast adaptive-weight aggregation method based on exponential step information propagation. The basic idea is to propagate information from long distance pixels within a few iterations. We also discuss important techniques of efficient implementation on GPU platform, which result in 10.5x speed up than a straightforward implementation. Compared to existing real time adaptive-weight approach, our technique reduces the computation time by more than half at improved accuracy. Detailed experimental results show that our technique is Pareto-optimal among existing real time or near real time stereo algorithms in the accuracy-speed trade-off space.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a local cost aggregation approach for real time stereo vision on a graphics processing unit (GPU). Recent research shows that local approaches based on carefully designed cost aggregation strategies can outperform many global approaches. Among those local aggregation approaches, adaptive-weight window produces the best quality disparity map under real-time constraint, […]<\/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":[73,33,3],"tags":[1791,1786,20,183],"class_list":["post-2194","post","type-post","status-publish","format-standard","hentry","category-computer-vision","category-image-processing","category-paper","tag-computer-vision","tag-image-processing","tag-nvidia","tag-nvidia-geforce-8800-gtx"],"views":1740,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2194","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=2194"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2194\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2194"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2194"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}