In this paper, we propose a real-time stereo matching method based on adaptive window, aiming at the trade-off between accuracy and efficiency in current local stereo matching, Considering that the Census transform has good adaptability to image amplitude distortion, but may introduce matching ambiguities in regions with noise or similar local structures, we combine the Census transform with AD (absolute differences) for matching cost initialization, and adopt an iterative cost aggregation method based on ESAW (exponential step adaptive weight), in order to improve the parallelism and execution efficiency. Furthermore, in disparity refinement stage, we build adaptive window based on pixel’s color similarity and Euclidean distance for each unreliable point. and classify the unreliable points as "occlusion" and "mismatch", so different refinement strategies could be taken for different classifications. Finally the proposed method is optimized with CUDA (compute unified device architecture) and evaluated on graphic processor. The experiment results show that the proposed method is the most accurate one compared with real-time stereo matching methods listed on the Middlebury stereo benchmark.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we propose a real-time stereo matching method based on adaptive window, aiming at the trade-off between accuracy and efficiency in current local stereo matching, Considering that the Census transform has good adaptability to image amplitude distortion, but may introduce matching ambiguities in regions with noise or similar local structures, we combine the […]<\/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":[11,89,3],"tags":[1782,14,20,1232,297,1103],"class_list":["post-10807","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gts-450","tag-real-time-graphics","tag-stereo-vision"],"views":3225,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10807","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=10807"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10807\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10807"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10807"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10807"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}