  This paper presents a new approach for synthesizing a novel street-view
panorama given an overhead satellite image. Taking a small satellite image
patch as input, our method generates a Google's omnidirectional street-view
type panorama, as if it is captured from the same geographical location as the
center of the satellite patch. Existing works tackle this task as an image
generation problem which adopts generative adversarial networks to implicitly
learn the cross-view transformations, while ignoring the domain relevance. In
this paper, we propose to explicitly establish the geometric correspondences
between the two-view images so as to facilitate the cross-view transformation
learning. Specifically, we observe that when a 3D point in the real world is
visible in both views, there is a deterministic mapping between the projected
points in the two-view images given the height information of this 3D point.
Motivated by this, we develop a novel Satellite to Street-view image Projection
(S2SP) module which explicitly establishes such geometric correspondences and
projects the satellite images to the street viewpoint. With these projected
satellite images as network input, we next employ a generator to synthesize
realistic street-view panoramas that are geometrically consistent with the
satellite images. Our S2SP module is differentiable and the whole framework is
trained in an end-to-end manner. Extensive experimental results on two
cross-view benchmark datasets demonstrate that our method generates images that
better respect the scene geometry than existing approaches.
