ConGeo: Robust Cross-view Geo-localization across Ground View Variations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916382472404992 |
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| author | Mi, Li Xu, Chang Castillo-Navarro, Javiera Montariol, Syrielle Yang, Wen Bosselut, Antoine Tuia, Devis |
| author_facet | Mi, Li Xu, Chang Castillo-Navarro, Javiera Montariol, Syrielle Yang, Wen Bosselut, Antoine Tuia, Devis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_13965 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ConGeo: Robust Cross-view Geo-localization across Ground View Variations Mi, Li Xu, Chang Castillo-Navarro, Javiera Montariol, Syrielle Yang, Wen Bosselut, Antoine Tuia, Devis Computer Vision and Pattern Recognition 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. |
| title | ConGeo: Robust Cross-view Geo-localization across Ground View Variations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.13965 |