CVGL: Causal Learning and Geometric Topology
Songsong Ouyang, Yingying Zhu
Cross-view geo-localization remains a challenging task due to drastic viewpoint and appearance discrepancies between ground-level and aerial (BEV) images. In this work, we propose a novel framework that fuses street-view and BEV representations to leverage their complementary information for robust localization. Our method introduces a feature fusion module that integrates BEV and ground-level features via residual-aware and spatial-attentive mechanisms. To further enhance the robustness of the street-view features, we incorporate a causal learning strategy that distills stable semantic cues through frequency-domain augmentation. The enhanced features are supervised by a lightweight Dice Loss branch while being detached from the main backpropagation to avoid disrupting the primary localization objective. Experimental results on benchmark datasets such as VIGOR and CVACT demonstrate that our approach significantly improves performance compared to baseline methods, particularly under challenging viewpoint variations.