{"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/360loc-a-dataset-and-benchmark-for","title":"360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries","arxiv_id":"2311.17389","date":"2023-11-29","proceeding":"CVPR 2024 1","authors":["Huajian Huang","Changkun Liu","Yipeng Zhu","Hui Cheng","Tristan Braud","Sai-Kit Yeung"],"abstract":"Portable 360$^\\circ$ cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene, these cameras could expedite building environment models that are essential for visual localization. However, such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset, 360Loc, composed of 360$^\\circ$ images with ground truth poses for visual localization. We present a practical implementation of 360$^\\circ$ mapping combining 360$^\\circ$ images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning, involving 360$^\\circ$ reference frames, and query frames from pinhole, ultra-wide FoV fisheye, and 360$^\\circ$ cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360$^\\circ$ images, which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap, and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries.","url_abs":"https://arxiv.org/abs/2311.17389v3","url_pdf":"https://arxiv.org/pdf/2311.17389v3.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":"360loc-a-dataset-and-benchmark-for","repo_url":"https://github.com/HuajianUP/360Loc","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"visual-localization","task_name":"Visual Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2311.17389","atlas_url":"https://app.syntology.ai/?focus=2311.17389","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}