  The goal of cross-view image based geo-localization is to determine the
location of a given street view image by matching it against a collection of
geo-tagged satellite images. This task is notoriously challenging due to the
drastic viewpoint and appearance differences between the two domains. We show
that we can address this discrepancy explicitly by learning to synthesize
realistic street views from satellite inputs. Following this observation, we
propose a novel multi-task architecture in which image synthesis and retrieval
are considered jointly. The rationale behind this is that we can bias our
network to learn latent feature representations that are useful for retrieval
if we utilize them to generate images across the two input domains. To the best
of our knowledge, ours is the first approach that creates realistic street
views from satellite images and localizes the corresponding query street-view
simultaneously in an end-to-end manner. In our experiments, we obtain
state-of-the-art performance on the CVUSA and CVACT benchmarks. Finally, we
show compelling qualitative results for satellite-to-street view synthesis.
