Abstract
Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientations and reduced field of views (FoVs). However, existing learning pipelines are orientation-specific or FoV-specific, demanding separate model training for different ground view variations. Such models heavily depend on the North-aligned spatial correspondence and predefined FoVs in the training data, compromising their robustness across different settings. To tackle this challenge, we propose ConGeo, a single- and cross-view Contrastive method for Geo-localization: it enhances robustness and consistency in feature representations to improve a model’s invariance to orientation and its resilience to FoV variations, by enforcing proximity between ground view variations of the same location. As a generic learning objective for cross-view geo-localization, when integrated into state-of-the-art pipelines, ConGeo significantly boosts the performance of three base models on four geo-localization benchmarks for diverse ground view variations and outperforms competing methods that train separate models for each ground view variation.
L. Mi and C. Xu—Equal contribution.
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Acknowledgments
We thank the anonymous reviewers for their constructive and thoughtful comments. We thank Haoyuan Li, Zimin Xia, Gencer Sümbül, Silin Gao, Valérie Zermatten, Zeming Chen, Tianqing Fang, Gaston Lenczner, Kuangyi Chen, Robin Zbinden, Giacomo May, Riccardo Ricci, Emanuele Dalsasso, and Sepideh Mamooler for providing helpful feedback on earlier versions of this work. We acknowledge the support from the CSC and EPFL Science Seed Fund and the support in part by the National Natural Science Foundation of China (NSFC) under Grant 62271355. AB gratefully acknowledges the support of the Swiss National Science Foundation (No. 215390), Innosuisse (PFFS-21-29), the EPFL Center for Imaging, Sony Group Corporation, and the Allen Institute for AI.
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Mi, L. et al. (2025). ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15072. Springer, Cham. https://doi.org/10.1007/978-3-031-72630-9_13
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