{"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/mods-fast-and-robust-method-for-two-view","title":"MODS: Fast and Robust Method for Two-View Matching","arxiv_id":"1503.02619","date":"2015-03-09","proceeding":null,"authors":["Dmytro Mishkin","Jiri Matas","Michal Perdoch"],"abstract":"A novel algorithm for wide-baseline matching called MODS - Matching On Demand\nwith view Synthesis - is presented. The MODS algorithm is experimentally shown\nto solve a broader range of wide-baseline problems than the state of the art\nwhile being nearly as fast as standard matchers on simple problems. The\napparent robustness vs. speed trade-off is finessed by the use of progressively\nmore time-consuming feature detectors and by on-demand generation of\nsynthesized images that is performed until a reliable estimate of geometry is\nobtained.\n  We introduce an improved method for tentative correspondence selection,\napplicable both with and without view synthesis. A modification of the standard\nfirst to second nearest distance rule increases the number of correct matches\nby 5-20% at no additional computational cost.\n  Performance of the MODS algorithm is evaluated on several standard publicly\navailable datasets, and on a new set of geometrically challenging wide baseline\nproblems that is made public together with the ground truth. Experiments show\nthat the MODS outperforms the state-of-the-art in robustness and speed.\nMoreover, MODS performs well on other classes of difficult two-view problems\nlike matching of images from different modalities, with wide temporal baseline\nor with significant lighting changes.","url_abs":"http://arxiv.org/abs/1503.02619v2","url_pdf":"http://arxiv.org/pdf/1503.02619v2.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":"mods-fast-and-robust-method-for-two-view","repo_url":"https://github.com/ducha-aiki/mods","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"mods-fast-and-robust-method-for-two-view","repo_url":"https://github.com/ducha-aiki/mods-light-zmq","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1503.02619","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}