ConGeo: Robust Cross-view Geo-localization across Ground View Variations
- URL: http://arxiv.org/abs/2403.13965v2
- Date: Wed, 4 Sep 2024 18:32:38 GMT
- Title: ConGeo: Robust Cross-view Geo-localization across Ground View Variations
- Authors: Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, Devis Tuia,
- Abstract summary: Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view.
Existing learning pipelines are orientation-specific or FoV-specific, demanding separate model training for different ground view variations.
We propose ConGeo, a single- and cross-view Contrastive method for Geo-localization.
- Score: 34.192775134189965
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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