RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918528656867328 |
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| author | Zheng, Junwei Dai, Ruize Liu, Ruiping Zeng, Zichao Chen, Yufan Wang, Fangjinhua Peng, Kunyu Yang, Kailun Zhang, Jiaming Stiefelhagen, Rainer |
| author_facet | Zheng, Junwei Dai, Ruize Liu, Ruiping Zeng, Zichao Chen, Yufan Wang, Fangjinhua Peng, Kunyu Yang, Kailun Zhang, Jiaming Stiefelhagen, Rainer |
| contents | Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27758 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization Zheng, Junwei Dai, Ruize Liu, Ruiping Zeng, Zichao Chen, Yufan Wang, Fangjinhua Peng, Kunyu Yang, Kailun Zhang, Jiaming Stiefelhagen, Rainer Computer Vision and Pattern Recognition Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole and satellite images, we study robust MCVGL using holistic panoramas and OpenStreetMap (OSM). To this end, we establish a large-scale MCVGL benchmark dataset, CV-RHO, with over 2.7M images under different weather and lighting conditions, as well as sensor noise. Furthermore, we propose a model termed RHO with a two-branch Pin-Pan architecture for accurate visual localization. A Split-Undistort-Merge (SUM) module is introduced to address the panoramic distortion, and a Position-Orientation Fusion (POF) mechanism is designed to enhance the localization accuracy. Extensive experiments prove the value of our CV-RHO dataset and the effectiveness of the RHO model, with a significant performance gain up to 20% compared with the state-of-the-art baselines. Project page: https://github.com/InSAI-Lab/RHO. |
| title | RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.27758 |