{"id":10456,"date":"2013-09-05T22:49:19","date_gmt":"2013-09-05T19:49:19","guid":{"rendered":"http:\/\/hgpu.org\/?p=10456"},"modified":"2013-09-05T22:49:19","modified_gmt":"2013-09-05T19:49:19","slug":"real-time-motion-artifact-compensation-for-pmd-tof-images","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10456","title":{"rendered":"Real-Time Motion Artifact Compensation for PMD-ToF Images"},"content":{"rendered":"<p>Time-of-Flight (ToF) cameras gained a lot of scientific attention and became a vivid field of research in the last years. A still remaining problem of ToF cameras are motion artifacts in dynamic scenes. This paper presents a new preprocessing method for a fast motion artifact compensation. We introduce a ow like algorithm that supports motion estimation, search field reduction and motion field optimization. The main focus lies on real-time processing capabilities. The approach is extensively tested and compared against other motion compensation techniques. For the evaluation, we use quantitative (ground-truth data, statistic error comparison) and qualitative (real environments, visual comparison) test methods. We show, that our proposed algorithm runs in real-time within a GPU based processing hardware (using NVIDIA Cuda) and corrects motion artifacts in a reliable way.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Time-of-Flight (ToF) cameras gained a lot of scientific attention and became a vivid field of research in the last years. A still remaining problem of ToF cameras are motion artifacts in dynamic scenes. This paper presents a new preprocessing method for a fast motion artifact compensation. We introduce a ow like algorithm that supports motion [&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":[89,33,3],"tags":[14,1786,126,20,1306],"class_list":["post-10456","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-motion-compensation","tag-nvidia","tag-nvidia-geforce-gtx-680"],"views":2515,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10456","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=10456"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10456\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10456"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10456"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10456"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}