The visual entities in cross-view images exhibit drastic domain changes due
to the difference in viewpoints each set of images is captured from. Existing
state-of-the-art methods address the problem by learning view-invariant
descriptors for the images. We propose a novel method for solving this task by
exploiting the generative powers of conditional GANs to synthesize an aerial
representation of a ground level panorama and use it to minimize the domain gap
between the two views. The synthesized image being from the same view as the
target image helps the network to preserve important cues in aerial images
following our Joint Feature Learning approach. Our Feature Fusion method
combines the complementary features from a synthesized aerial image with the
corresponding ground features to obtain a robust query representation. In
addition, multi-scale feature aggregation preserves image representations at
different feature scales useful for solving this complex task. Experimental
results show that our proposed approach performs significantly better than the
state-of-the-art methods on the challenging CVUSA dataset in terms of top-1 and
top-1% retrieval accuracies. Furthermore, to evaluate the generalization of our
method on urban landscapes, we collected a new cross-view localization dataset
with geo-reference information.

本研究提出了一种基于条件 GAN 的新方法，通过合成俯视图像，将两个视图之间的差距最小化，实现了对视觉实体的跨视图建模并进行特征融合，最终在 CVUSA 数据集上成功实现了景点检索任务。