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This work addresses cross-view camera pose estimation, i.e., determining the3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerialimage of the local area. We propose SliceMatch, which consists of ground andaerial feature extractors, feature aggregators, and a pose predictor. Thefeature extractors extract dense features from the ground and aerial images.Given a set of candidate camera poses, the feature aggregators construct asingle ground descriptor and a set of pose-dependent aerial descriptors.Notably, our novel aerial feature aggregator has a cross-view attention modulefor ground-view guided aerial feature selection and utilizes the geometricprojection of the ground camera's viewing frustum on the aerial image to poolfeatures. The efficient construction of aerial descriptors is achieved usingprecomputed masks. SliceMatch is trained using contrastive learning and poseestimation is formulated as a similarity comparison between the grounddescriptor and the aerial descriptors. Compared to the state-of-the-art,SliceMatch achieves a 19% lower median localization error on the VIGORbenchmark using the same VGG16 backbone at 150 frames per second, and a 50%lower error when using a ResNet50 backbone.

SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation