  We propose a novel end-to-end method for cross-view pose estimation. Given a
ground-level query image and an aerial image that covers the query's local
neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by
matching its image descriptor to descriptors of local regions within the aerial
image. The orientation-aware descriptors are obtained by using a
translationally equivariant convolutional ground image encoder and contrastive
learning. The Localization Decoder produces a dense probability distribution in
a coarse-to-fine manner with a novel Localization Matching Upsampling module. A
smaller Orientation Decoder produces a vector field to condition the
orientation estimate on the localization. Our method is validated on the VIGOR
and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and
36% in median localization error for comparable orientation estimation
accuracy. The predicted probability distribution can represent localization
ambiguity, and enables rejecting possible erroneous predictions. Without
re-training, the model can infer on ground images with different field of views
and utilize orientation priors if available. On the Oxford RobotCar dataset,
our method can reliably estimate the ego-vehicle's pose over time, achieving a
median localization error under 1 meter and a median orientation error of
around 1 degree at 14 FPS.
