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arXiv · 2509.20684

Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance

Xiaowei Wang·Di Wang·Ke Li·Yifeng Wang·Chengjian Wang·Libin Sun·Zhihong Wu·Yiming Zhang·Quan Wang
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Abstract

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.

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Xiaowei Wang, Di Wang, Ke Li, Yifeng Wang, Chengjian Wang, Libin Sun, Zhihong Wu, Yiming Zhang, Quan Wang. 2025-09-25. Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance. https://arxiv.org/abs/2509.20684

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