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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.20684 |
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| _version_ | 1866914055801798656 |
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| author | Wang, Xiaowei Wang, Di Li, Ke Wang, Yifeng Wang, Chengjian Sun, Libin Wu, Zhihong Zhang, Yiming Wang, Quan |
| author_facet | Wang, Xiaowei Wang, Di Li, Ke Wang, Yifeng Wang, Chengjian Sun, Libin Wu, Zhihong Zhang, Yiming Wang, Quan |
| contents | Cross-view geo-localization (CVGL) aims to match images of the same location captured from drastically different viewpoints. Despite recent progress, existing methods still face two key challenges: (1) achieving robustness under severe appearance variations induced by diverse UAV orientations and fields of view, which hinders cross-domain generalization, and (2) establishing reliable correspondences that capture both global scene-level semantics and fine-grained local details. In this paper, we propose EGS, a novel CVGL framework designed to enhance cross-domain generalization. Specifically, we introduce an E(2)-Steerable CNN encoder to extract stable and reliable features under rotation and viewpoint shifts. Furthermore, we construct a graph with a virtual super-node that connects to all local nodes, enabling global semantics to be aggregated and redistributed to local regions, thereby enforcing global-local consistency. Extensive experiments on the University-1652 and SUES-200 benchmarks demonstrate that EGS consistently achieves substantial performance gains and establishes a new state of the art in cross-domain CVGL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance Wang, Xiaowei Wang, Di Li, Ke Wang, Yifeng Wang, Chengjian Sun, Libin Wu, Zhihong Zhang, Yiming Wang, Quan Computer Vision and Pattern Recognition Cross-view geo-localization (CVGL) aims to match images of the same location captured from drastically different viewpoints. Despite recent progress, existing methods still face two key challenges: (1) achieving robustness under severe appearance variations induced by diverse UAV orientations and fields of view, which hinders cross-domain generalization, and (2) establishing reliable correspondences that capture both global scene-level semantics and fine-grained local details. In this paper, we propose EGS, a novel CVGL framework designed to enhance cross-domain generalization. Specifically, we introduce an E(2)-Steerable CNN encoder to extract stable and reliable features under rotation and viewpoint shifts. Furthermore, we construct a graph with a virtual super-node that connects to all local nodes, enabling global semantics to be aggregated and redistributed to local regions, thereby enforcing global-local consistency. Extensive experiments on the University-1652 and SUES-200 benchmarks demonstrate that EGS consistently achieves substantial performance gains and establishes a new state of the art in cross-domain CVGL. |
| title | Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.20684 |