Test-Time Denoising for Noisy Cross-view Geo-localization

Cross-view geo-localization (CVGL) aims to localize a ground-level query by retrieving its corresponding image from a geo-tagged satellite gallery. Although recent methods achieve strong performance on clean benchmarks, their accuracy can degrade when query images are affected by sensor, acquisition, or transmission noise. Existing noise-robustness solutions typically rely on training strategies or specialized model architectures, which require modifying or retraining the downstream localization system. In this work, we adopt a training-free and plug-and-play strategy from a shared denoising perspective. We distinguish noise-dependent observation statistics from the shared underlying clean-image target and prepend a frozen pretrained denoiser to existing CVGL models while keeping the localization model unchanged. The same pretrained denoiser is applied across heterogeneous query-side noise without noise-specific adaptation. Extensive experiments across multiple CVGL architectures, noise types, and severity levels show that pretrained denoising can improve localization robustness, with the gains becoming more pronounced under stronger noise. These results demonstrate the potential of pretrained denoising as a simple test-time strategy for improving the noise robustness of frozen CVGL models without retraining or modifying the localization network.