{"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/backtracking-regression-forests-for-accurate","title":"Backtracking Regression Forests for Accurate Camera Relocalization","arxiv_id":"1710.07965","date":"2017-10-22","proceeding":null,"authors":["Lili Meng","Jianhui Chen","Frederick Tung","James J. Little","Julien Valentin","Clarence W. de Silva"],"abstract":"Camera relocalization plays a vital role in many robotics and computer vision\ntasks, such as global localization, recovery from tracking failure, and loop\nclosure detection. Recent random forests based methods directly predict 3D\nworld locations for 2D image locations to guide the camera pose optimization.\nDuring training, each tree greedily splits the samples to minimize the spatial\nvariance. However, these greedy splits often produce uneven sub-trees in\ntraining or incorrect 2D-3D correspondences in testing. To address these\nproblems, we propose a sample-balanced objective to encourage equal numbers of\nsamples in the left and right sub-trees, and a novel backtracking scheme to\nremedy the incorrect 2D-3D correspondence predictions. Furthermore, we extend\nthe regression forests based methods to use local features in both training and\ntesting stages for outdoor RGB-only applications. Experimental results on\npublicly available indoor and outdoor datasets demonstrate the efficacy of our\napproach, which shows superior or on-par accuracy with several state-of-the-art\nmethods.","url_abs":"http://arxiv.org/abs/1710.07965v1","url_pdf":"http://arxiv.org/pdf/1710.07965v1.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":[{"paper_slug":"backtracking-regression-forests-for-accurate","repo_url":"https://github.com/LiliMeng/btrf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"camera-relocalization","task_name":"Camera Relocalization"},{"task_slug":"loop-closure-detection","task_name":"Loop Closure Detection"},{"task_slug":"simultaneous-localization-and-mapping","task_name":"Simultaneous Localization and Mapping"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.07965","atlas_url":"https://app.syntology.ai/?focus=1710.07965","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}