{"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/unos-unified-unsupervised-optical-flow-and","title":"UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Yang Wang"," Peng Wang"," Zhenheng Yang"," Chenxu Luo"," Yi Yang"," Wei Xu"],"abstract":"In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption. UnOS significantly outperforms other state-of-the-art (SOTA) unsupervised approaches that treated the two tasks independently. Specifically, given two consecutive stereo image pairs from a video, UnOS estimates per-pixel stereo depth images, camera ego-motion and optical flow with three parallel CNNs. Based on these quantities, UnOS computes rigid optical flow and compares it against the optical flow estimated from the FlowNet, yielding pixels satisfying the rigid-scene assumption. Then, we encourage geometrical consistency between the two estimated flows within rigid regions, from which we derive a rigid-aware direct visual odometry (RDVO) module. We also propose rigid and occlusion-aware flow-consistency losses for the learning of UnOS. We evaluated our results on the popular KITTI dataset over 4 related tasks, i.e. stereo depth, optical flow, visual odometry and motion segmentation.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_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":[{"paper_slug":"unos-unified-unsupervised-optical-flow-and","repo_url":"https://github.com/baidu-research/UnDepthflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"motion-segmentation","task_name":"Motion Segmentation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"},{"task_slug":"stereo-depth-estimation","task_name":"Stereo Depth Estimation"},{"task_slug":"visual-odometry","task_name":"Visual Odometry"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}