{"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/efficient-outlier-removal-in-large-scale","title":"Efficient Outlier Removal in Large Scale Global Structure-from-Motion","arxiv_id":"1808.03041","date":"2018-08-09","proceeding":null,"authors":["Fei Wen","Danping Zou","Rendong Ying","Peilin Liu"],"abstract":"This work addresses the outlier removal problem in large-scale global\nstructure-from-motion. In such applications, global outlier removal is very\nuseful to mitigate the deterioration caused by mismatches in the feature point\nmatching step. Unlike existing outlier removal methods, we exploit the\nstructure in multiview geometry problems to propose a dimension reduced\nformulation, based on which two methods have been developed. The first method\nconsiders a convex relaxed $\\ell_1$ minimization and is solved by a single\nlinear programming (LP), whilst the second one approximately solves the ideal\n$\\ell_0$ minimization by an iteratively reweighted method. The dimension\nreduction results in a significant speedup of the new algorithms. Further, the\niteratively reweighted method can significantly reduce the possibility of\nremoving true inliers. Realistic multiview reconstruction experiments\ndemonstrated that, compared with state-of-the-art algorithms, the new\nalgorithms are much more efficient and meanwhile can give improved solution.\nMatlab code for reproducing the results is available at\n\\textit{https://github.com/FWen/OUTLR.git}.","url_abs":"http://arxiv.org/abs/1808.03041v4","url_pdf":"http://arxiv.org/pdf/1808.03041v4.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":"efficient-outlier-removal-in-large-scale","repo_url":"https://github.com/FWen/OUTLR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}