{"id":60,"date":"2019-12-09T21:54:02","date_gmt":"2019-12-09T21:54:02","guid":{"rendered":"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/?page_id=60"},"modified":"2019-12-14T01:56:53","modified_gmt":"2019-12-14T01:56:53","slug":"single-view-3d-keypoint-reconstruction-for-vehicles","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/single-view-3d-keypoint-reconstruction-for-vehicles\/","title":{"rendered":"Single-view 3D Keypoint Reconstruction for Vehicles (Xudong Chen, Fangyu Li)"},"content":{"rendered":"<p>The aim of this project is to recover 3D poses and the shape of vehicles at the intersections in contribution to the &#8220;Open Vision Platform for Smart City&#8221;. Successful estimation of\u00a0<span style=\"font-weight: 400\">3D information of the vehicles open possibilities for many intelligent city applications, such as t<\/span><span style=\"font-size: 1rem\">raffic analytics, vehicle localization, velocity estimation, abnormal detection, trajectory prediction. Furthermore, using a single view to reconstruct the 3D shape and pose of the object is very challenging and has been extensively studied in the computer vision community. In this project, we utilize the vehicle trajectory in the video sequence as an additional cue to rectify the poses.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>The algorithm pipeline is as follow:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-61 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.53.15-PM-300x111.png\" alt=\"\" width=\"384\" height=\"142\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.53.15-PM-300x111.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.53.15-PM-768x284.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.53.15-PM-1024x379.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.53.15-PM.png 1572w\" sizes=\"auto, (max-width: 384px) 100vw, 384px\" \/><\/p>\n<p>the initial pose is estimated by solvePnP with car-centric RANSAC:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-69 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-300x300.png\" alt=\"\" width=\"300\" height=\"300\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-300x300.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-150x150.png 150w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-768x768.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1024x1024.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13.png 1200w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-100x100.png 100w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>to optimize the 3D shape and pose of each vehicle, we use\u00a0Lavern Marquardt (LM) optimization to jointly optimize the shape coefficients and poses. The loss terms are as follow:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-65 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.55.35-PM-300x116.png\" alt=\"\" width=\"300\" height=\"116\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.55.35-PM-300x116.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.55.35-PM-768x296.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.55.35-PM-1024x394.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-4.55.35-PM.png 1496w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>and the result of the optimization is shown below:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-70 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-300x300.png\" alt=\"\" width=\"300\" height=\"300\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-300x300.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-150x150.png 150w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-768x768.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-1024x1024.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1.png 1200w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-1-100x100.png 100w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>after obtaining the pose of each vehicle at different timestamps, we could fit their trajectories into a second-order spline which is obtained by the 3D poses estimated from optimization at each timestamp:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-66 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-5.04.58-PM-300x135.png\" alt=\"\" width=\"300\" height=\"135\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-5.04.58-PM-300x135.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-5.04.58-PM-768x347.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-5.04.58-PM-1024x462.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/Screenshot-2019-12-09-at-5.04.58-PM.png 1236w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>finally, we visualize the effect of pose rectification from the fitted trajectory:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-71 aligncenter\" src=\"http:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-300x300.png\" alt=\"\" width=\"300\" height=\"300\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-300x300.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-150x150.png 150w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-768x768.png 768w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-1024x1024.png 1024w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2.png 1200w, https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/wp-content\/uploads\/sites\/30\/2019\/12\/3d_all_13-2-100x100.png 100w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/p>\n<p>A visualization of the 3D reconstruction could be seen here:<\/p>\n<p>https:\/\/youtu.be\/p0C2z-WgogU<\/p>\n<p>The video of the presentation:<\/p>\n<p>https:\/\/youtu.be\/VEPrXie8-PU<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The aim of this project is to recover 3D poses and the shape of vehicles at the intersections in contribution to the &#8220;Open Vision Platform for Smart City&#8221;. Successful estimation of\u00a03D information of the vehicles open possibilities for many intelligent city applications, such as traffic analytics, vehicle localization, velocity estimation, abnormal detection, trajectory prediction. Furthermore, &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/single-view-3d-keypoint-reconstruction-for-vehicles\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Single-view 3D Keypoint Reconstruction for Vehicles (Xudong Chen, Fangyu Li)&#8221;<\/span><\/a><\/p>\n","protected":false},"author":66,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-60","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Single-view 3D Keypoint Reconstruction for Vehicles (Xudong Chen, Fangyu Li) - Open Vision Platform for Smart City Intersections &amp; VQA from Camera Networks<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2019teamo\/single-view-3d-keypoint-reconstruction-for-vehicles\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Single-view 3D Keypoint Reconstruction for Vehicles (Xudong Chen, Fangyu Li) - Open Vision Platform for Smart City Intersections &amp; VQA from Camera Networks\" \/>\n<meta property=\"og:description\" content=\"The aim of this project is to recover 3D poses and the shape of vehicles at the intersections in contribution to the &#8220;Open Vision Platform for Smart City&#8221;. Successful estimation of\u00a03D information of the vehicles open possibilities for many intelligent city applications, such as traffic analytics, vehicle localization, velocity estimation, abnormal detection, trajectory prediction. 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