{"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/optical-flow-in-mostly-rigid-scenes","title":"Optical Flow in Mostly Rigid Scenes","arxiv_id":"1705.01352","date":"2017-05-03","proceeding":"CVPR 2017 7","authors":["Jonas Wulff","Laura Sevilla-Lara","Michael J. Black"],"abstract":"The optical flow of natural scenes is a combination of the motion of the\nobserver and the independent motion of objects. Existing algorithms typically\nfocus on either recovering motion and structure under the assumption of a\npurely static world or optical flow for general unconstrained scenes. We\ncombine these approaches in an optical flow algorithm that estimates an\nexplicit segmentation of moving objects from appearance and physical\nconstraints. In static regions we take advantage of strong constraints to\njointly estimate the camera motion and the 3D structure of the scene over\nmultiple frames. This allows us to also regularize the structure instead of the\nmotion. Our formulation uses a Plane+Parallax framework, which works even under\nsmall baselines, and reduces the motion estimation to a one-dimensional search\nproblem, resulting in more accurate estimation. In moving regions the flow is\ntreated as unconstrained, and computed with an existing optical flow method.\nThe resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art\nresults on both the MPI-Sintel and KITTI-2015 benchmarks.","url_abs":"http://arxiv.org/abs/1705.01352v1","url_pdf":"http://arxiv.org/pdf/1705.01352v1.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":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/optical-flow-estimation-on-sintel-clean","task":"Optical Flow Estimation","dataset":"Sintel-clean","model":"MR-Flow","rank_in_archive_order":15,"of":29,"metrics":{"Average End-Point Error":"2.53"},"uses_additional_data":false},{"leaderboard":"/sota/optical-flow-estimation-on-sintel-final","task":"Optical Flow Estimation","dataset":"Sintel-final","model":"MR-Flow","rank_in_archive_order":24,"of":28,"metrics":{"Average End-Point Error":"5.38"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.01352","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}