{"id":3993,"date":"2011-05-17T14:47:18","date_gmt":"2011-05-17T14:47:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=3993"},"modified":"2011-05-17T14:47:18","modified_gmt":"2011-05-17T14:47:18","slug":"high-resolution-stereo-video-rectification-through-a-cost-efficient-real-time-gpu-implementation-using-intrinsic-and-extrinsic-camera-parameters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3993","title":{"rendered":"High-resolution stereo video rectification through a cost-efficient real-time GPU implementation using intrinsic and extrinsic camera parameters"},"content":{"rendered":"<p>This paper describes a method to correct the distortions in the images captured by a stereo video camera rig. We developed a code which can be implemented both on CPUs and GPUs which are used respectively to process still images or videos. The processing scheme is centered on the pre-calculation of a deformation texture, based on the physical properties of the camera setup (intrinsic parameters). The pre-calculated texture contains deformation parameters for each pixel. The GPU implementation of our method is cost-efficient in terms of computer resources and can therefore be used to rectify high-resolution image pairs in real-time. Moreover, due to the pre-computation step, any camera model of arbitrary complexity can be used without impacting the execution frame rate.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes a method to correct the distortions in the images captured by a stereo video camera rig. We developed a code which can be implemented both on CPUs and GPUs which are used respectively to process still images or videos. The processing scheme is centered on the pre-calculation of a deformation texture, based [&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":[33,3],"tags":[1786],"class_list":["post-3993","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-paper","tag-image-processing"],"views":1801,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3993","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=3993"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3993\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3993"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3993"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3993"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}