{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-multi-view-camera-relocalization","title":"Learning Multi-View Camera Relocalization With Graph Neural Networks","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Fei Xue"," Xin Wu"," Shaojun Cai"," Junqiu Wang"],"abstract":"We propose to construct a view graph to excavate the information of the whole given sequence for absolute camera pose estimation. Specifically, we harness GNNs to model the graph, allowing even non-consecutive frames to exchange information with each other. Rather than adopting the regular GNNs directly, we redefine the nodes, edges, and embedded functions to fit the relocalization task. Redesigned GNNs cooperate with CNNs in guiding knowledge propagation and feature extraction respectively to process multi-view high-dimension image features iteratively at different levels. Besides, a general graph-based loss function beyond constraints between consecutive views is employed for training the network in an end-to-end fashion. Extensive experiments conducted on both indoor and outdoor datasets demonstrate that our method outperforms previous approaches especially in large-scale and challenging scenarios.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Xue_Learning_Multi-View_Camera_Relocalization_With_Graph_Neural_Networks_CVPR_2020_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"camera-localization","task_name":"Camera Localization"},{"task_slug":"camera-pose-estimation","task_name":"Camera Pose Estimation"},{"task_slug":"camera-relocalization","task_name":"Camera Relocalization"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"visual-localization","task_name":"Visual Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/camera-localization-on-oxford-robotcar-full","task":"Camera Localization","dataset":"Oxford RobotCar Full","model":"GNNMapNet","rank_in_archive_order":3,"of":6,"metrics":{"Mean Translation Error":"17.35"},"uses_additional_data":false},{"leaderboard":"/sota/visual-localization-on-oxford-robotcar-full","task":"Visual Localization","dataset":"Oxford RobotCar Full","model":"GNNMapNet","rank_in_archive_order":4,"of":6,"metrics":{"Mean Translation Error":"17.35"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}