{"id":4896,"date":"2011-07-27T00:18:36","date_gmt":"2011-07-26T21:18:36","guid":{"rendered":"http:\/\/hgpu.org\/?p=4896"},"modified":"2011-07-27T00:18:36","modified_gmt":"2011-07-26T21:18:36","slug":"a-very-fast-census-based-stereo-matching-implementation-on-a-graphics-processing-unit","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4896","title":{"rendered":"A very fast census-based stereo matching implementation on a graphics processing unit"},"content":{"rendered":"<p>In this paper a very fast graphics processing unit implementation of a local, census-correlation-based stereo matching algorithm is presented. In comparison to absolute or squared difference correlation techniques, the census transform is computational more expensive which led to the motivation of a GPU-based implementation. Due to the parallel architecture of modern graphics cards, complex algorithms can be executed very efficiently. Thus, this work deals with the question how to use a GPU for high speed and high quality stereo matching. The proposed implementation achieves 75.7 fps at an image resolution of 640 x 480 and a disparity search range of 50 on a NVIDIA GeForce GTX 280 graphics card.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper a very fast graphics processing unit implementation of a local, census-correlation-based stereo matching algorithm is presented. In comparison to absolute or squared difference correlation techniques, the census transform is computational more expensive which led to the motivation of a GPU-based implementation. Due to the parallel architecture of modern graphics cards, complex algorithms [&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":[36,11,3],"tags":[1787,1782,20,234,1103],"class_list":["post-4896","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-stereo-vision"],"views":2339,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4896","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=4896"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4896\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4896"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4896"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}