ConGeo: Robust Cross-view Geo-localization across Ground View Variations
<|reference_start|>ConGeo: Robust Cross-view Geo-localization across Ground View Variations: 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.<|reference_end|>
@article{mi2024congeo:,
    title={ConGeo: Robust Cross-view Geo-localization across Ground View Variations},
    author={Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen
  Yang, Antoine Bosselut, Devis Tuia},
    journal={arXiv preprint arXiv:2403.13965},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13965},
    primaryClass={cs.CV}
}