CVGL: Causal Learning and Geometric Topology
This work addresses a critical problem for autonomous navigation and mapping by improving geo-localization accuracy in complex scenarios, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles cross-view geo-localization by matching street and aerial images, proposing the CLGT framework that integrates causal learning to reduce confounding factors and geometric topology fusion to address viewpoint differences, achieving state-of-the-art performance on benchmarks like CVUSA and CVACT with robustness to real-world corruptions.
Cross-view geo-localization (CVGL) aims to estimate the geographic location of a street image by matching it with a corresponding aerial image. This is critical for autonomous navigation and mapping in complex real-world scenarios. However, the task remains challenging due to significant viewpoint differences and the influence of confounding factors. To tackle these issues, we propose the Causal Learning and Geometric Topology (CLGT) framework, which integrates two key components: a Causal Feature Extractor (CFE) that mitigates the influence of confounding factors by leveraging causal intervention to encourage the model to focus on stable, task-relevant semantics; and a Geometric Topology Fusion (GT Fusion) module that injects Bird's Eye View (BEV) road topology into street features to alleviate cross-view inconsistencies caused by extreme perspective changes. Additionally, we introduce a Data-Adaptive Pooling (DA Pooling) module to enhance the representation of semantically rich regions. Extensive experiments on CVUSA, CVACT, and their robustness-enhanced variants (CVUSA-C-ALL and CVACT-C-ALL) demonstrate that CLGT achieves state-of-the-art performance, particularly under challenging real-world corruptions. Our codes are available at https://github.com/oyss-szu/CLGT.