  This work addresses cross-view camera pose estimation, i.e., determining the
3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial
image of the local area. We propose SliceMatch, which consists of ground and
aerial feature extractors, feature aggregators, and a pose predictor. The
feature extractors extract dense features from the ground and aerial images.
Given a set of candidate camera poses, the feature aggregators construct a
single ground descriptor and a set of pose-dependent aerial descriptors.
Notably, our novel aerial feature aggregator has a cross-view attention module
for ground-view guided aerial feature selection and utilizes the geometric
projection of the ground camera's viewing frustum on the aerial image to pool
features. The efficient construction of aerial descriptors is achieved using
precomputed masks. SliceMatch is trained using contrastive learning and pose
estimation is formulated as a similarity comparison between the ground
descriptor and the aerial descriptors. Compared to the state-of-the-art,
SliceMatch achieves a 19% lower median localization error on the VIGOR
benchmark using the same VGG16 backbone at 150 frames per second, and a 50%
lower error when using a ResNet50 backbone.
