Shi, YujiaoYu, XinCampbell, DylanLi, Hongdong2025-05-232025-05-231063-6919ORCID:/0000-0002-4717-6850/work/160891735ORCID:/0000-0003-4125-1554/work/163239716http://www.scopus.com/inward/record.url?scp=85094126931&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733753183Cross-view geo-localization is the problem of estimating the position and orientation (latitude, longitude and azimuth angle) of a camera at ground level given a large-scale database of geo-tagged aerial (e.g., satellite) images. Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment. It is well-recognized that knowing the orientation between ground and aerial images can significantly reduce matching ambiguity between these two views, especially when the ground-level images have a limited Field of View (FoV) instead of a full field-of-view panorama. Therefore, we design a Dynamic Similarity Matching network to estimate cross-view orientation alignment during localization. In particular, we address the cross-view domain gap by applying a polar transform to the aerial images to approximately align the images up to an unknown azimuth angle. Then, a two-stream convolutional network is used to learn deep features from the ground and polar-transformed aerial images. Finally, we obtain the orientation by computing the correlation between cross-view features, which also provides a more accurate measure of feature similarity, improving location recall. Experiments on standard datasets demonstrate that our method significantly improves state-of-the-art performance. Remarkably, we improve the top-1 location recall rate on the CVUSA dataset by a factor of 1.5× for panoramas with known orientation, by a factor of 3.3× for panoramas with unknown orientation, and by a factor of 6× for 180◦-FoV images with unknown orientation.This research is supported in part by the Australian Research Council (ARC) Centre of Excellence for Robotic Vision (CE140100016), ARC-Discovery (DP 190102261) and ARC-LIEF (190100080), as well as a research grant from Baidu on autonomous driving. The first author is a China Scholarship Council (CSC)-funded PhD student to ANU. We gratefully acknowledge the GPUs donated by the NVIDIA Corporation. We thank all anonymous reviewers and ACs for their constructive comments.9enPublisher Copyright: © 2020 IEEE.202010.1109/CVPR42600.2020.0041285094126931