Existing deep methods overlook those appearance and geometric differences, and instead use a brute force training procedure, leading to inferior performance.

One of the key reasons is the vast differences between the two view modalities, i.e.,

However, these two aspects in conjunction with a non crystal clear experimental setup raise some questions.

In this paper, we develop a new deep network to explicitly address these inherent differences between ground and aerial views. We observe there exist some approximate domain correspondences between ground and aerial images. Specifically, pixels lying on the same azimuth direction in an aerial image approximately correspond to a vertical image column in the ground view image. Thus, we propose a two-step approach to exploit this prior knowledge. The first step is to apply a regular polar transform to warp an aerial image such that its domain is closer to that of a ground-view panorama.