  Despite the significant progress in 6-DoF visual localization, researchers
are mostly driven by ground-level benchmarks. Compared with aerial oblique
photography, ground-level map collection lacks scalability and complete
coverage. In this work, we propose to go beyond the traditional ground-level
setting and exploit the cross-view localization from aerial to ground. We solve
this problem by formulating camera pose estimation as an iterative
render-and-compare pipeline and enhancing the robustness through augmenting
seeds from noisy initial priors. As no public dataset exists for the studied
problem, we collect a new dataset that provides a variety of cross-view images
from smartphones and drones and develop a semi-automatic system to acquire
ground-truth poses for query images. We benchmark our method as well as several
state-of-the-art baselines and demonstrate that our method outperforms other
approaches by a large margin.
