CoVisFormer: Co-Visibility-Aware Correspondence Learning for Cross-View Geo-Localization

Estimating 3-DoF pose between a ground image and an aerial image is vital for a variety of computer vision applications. However, achieving robust cross-view geo-localization remains challenging because the limited cross-view interaction results in erroneous matches in non-overlapping regions, and fixed rules for inlier selection limit the effective rejection of outliers. To deal with these issues, a novel cross-view geo-localization method is proposed by integrating cross-view interaction with dynamic inlier detection, including a co-visibility-aware cross-view contextual feature learning (C3FL) module and a dynamic inlier detection-based pose regression (DIDP) module. The proposed CoVisFormer enjoys several merits. First, the proposed C3FL module can leverage cross-view interaction to focus matching on overlapping regions. % Second, the DIDP module can adaptively update according to the current input, enabling effective outlier rejection. % Extensive experimental results on two challenging benchmarks show that our proposed method significantly outperforms state-of-the-art cross-view geo-localization methods.