{"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/beyond-geo-localization-fine-grained","title":"Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials","arxiv_id":"2307.03398","date":"2023-07-07","proceeding":null,"authors":["Wenmiao Hu","Yichen Zhang","Yuxuan Liang","Yifang Yin","Andrei Georgescu","An Tran","Hannes Kruppa","See-Kiong Ng","Roger Zimmermann"],"abstract":"Street-view imagery provides us with novel experiences to explore different places remotely. Carefully calibrated street-view images (e.g. Google Street View) can be used for different downstream tasks, e.g. navigation, map features extraction. As personal high-quality cameras have become much more affordable and portable, an enormous amount of crowdsourced street-view images are uploaded to the internet, but commonly with missing or noisy sensor information. To prepare this hidden treasure for \"ready-to-use\" status, determining missing location information and camera orientation angles are two equally important tasks. Recent methods have achieved high performance on geo-localization of street-view images by cross-view matching with a pool of geo-referenced satellite imagery. However, most of the existing works focus more on geo-localization than estimating the image orientation. In this work, we re-state the importance of finding fine-grained orientation for street-view images, formally define the problem and provide a set of evaluation metrics to assess the quality of the orientation estimation. We propose two methods to improve the granularity of the orientation estimation, achieving 82.4% and 72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement compared to previous works. Integrating fine-grained orientation estimation in training also improves the performance on geo-localization, giving top 1 recall 95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two datasets.","url_abs":"https://arxiv.org/abs/2307.03398v2","url_pdf":"https://arxiv.org/pdf/2307.03398v2.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":"image-based-localization","task_name":"Image-Based Localization"},{"task_slug":"geo-localization","task_name":"geo-localization"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-based-localization-on-cvact","task":"Image-Based Localization","dataset":"cvact","model":"GeoDTR","rank_in_archive_order":4,"of":8,"metrics":{"Recall@1":"86.21","Recall@1 (%)":"98.77","Recall@10":"96.72","Recall@5":"95.44"},"uses_additional_data":false},{"leaderboard":"/sota/image-based-localization-on-cvusa-1","task":"Image-Based Localization","dataset":"cvusa","model":"GeoDTR","rank_in_archive_order":4,"of":8,"metrics":{"Recall@1":"95.43","Recall@10":"99.34","Recall@5":"98.86","Recall@top1%":"99.86"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.03398","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}