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The visual entities in cross-view images exhibit drastic domain changes dueto the difference in viewpoints each set of images is captured from. Existingstate-of-the-art methods address the problem by learning view-invariantdescriptors for the images. We propose a novel method for solving this task byexploiting the generative powers of conditional GANs to synthesize an aerialrepresentation of a ground level panorama and use it to minimize the domain gapbetween the two views. The synthesized image being from the same view as thetarget image helps the network to preserve important cues in aerial imagesfollowing our Joint Feature Learning approach. Our Feature Fusion methodcombines the complementary features from a synthesized aerial image with thecorresponding ground features to obtain a robust query representation. Inaddition, multi-scale feature aggregation preserves image representations atdifferent feature scales useful for solving this complex task. Experimentalresults show that our proposed approach performs significantly better than thestate-of-the-art methods on the challenging CVUSA dataset in terms of top-1 andtop-1% retrieval accuracies. Furthermore, to evaluate the generalization of ourmethod on urban landscapes, we collected a new cross-view localization datasetwith geo-reference information.

Bridging the Domain Gap for Ground-to-Aerial Image Matching