{"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/omnistereo-real-time-omnidireactional-depth","title":"OmniStereo: Real-time Omnidireactional Depth Estimation with Multiview Fisheye Cameras","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Jiaxi Deng","Yushen Wang","Haitao Meng","Zuoxun Hou","Yi Chang","Gang Chen"],"abstract":"    Fast and reliable omnidirectional 3D sensing is essential to many applications such as autonomous driving, robotics and drone navigation. While many well-recognized methods have been developed to produce high-quality omnidirectional 3D information, they are too slow for real-time computation, limiting their feasibility in practical applications. Motivated by these shortcomings, we propose an efficient omnidirectional depth sensing framework, called OmniStereo, which generates high-quality 3D information in real-time. Unlike prior works, OmniStereo employs Cassini projection to simplify the photometric matching and introduces a lightweight stereo matching network to minimize computational overhead. Additionally, OmniStereo proposes a novel fusion method to handle depth discontinuities and invalid pixels complemented by a refinement module to reduce mapping-introduced errors and recover fine details. As a result, OmniStereo achieves state-of-the-art (SOTA) accuracy, surpassing the second-best method over 32% in MAE, while maintaining real-time efficiency. It operates more than 16.5xfaster than the second-best method in accuracy on TITAN RTX, achieving 12.3 FPS on embedded device Jetson AGX Orin, underscoring its suitability for real-world deployment. The code is available at https://github.com/DengJiaxi1/OmniStereo.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Deng_OmniStereo_Real-time_Omnidireactional_Depth_Estimation_with_Multiview_Fisheye_Cameras_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Deng_OmniStereo_Real-time_Omnidireactional_Depth_Estimation_with_Multiview_Fisheye_Cameras_CVPR_2025_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":"omnistereo-real-time-omnidireactional-depth","repo_url":"https://github.com/dengjiaxi1/omnistereo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"drone-navigation","task_name":"Drone navigation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[{"method_slug":"mae","method_name":"MAE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}