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To find the geolocation of a street-view image, cross-view geolocalization(CVGL) methods typically perform image retrieval on a database of georeferencedaerial images and determine the location from the visually most similar match.Recent approaches focus mainly on settings where street-view and aerial imagesare preselected to align w.r.t. translation or orientation, but struggle inchallenging real-world scenarios where varying camera poses have to be matchedto the same aerial image. We propose a novel trainable retrieval architecturethat uses bird's eye view (BEV) maps rather than vectors as embeddingrepresentation, and explicitly addresses the many-to-one ambiguity that arisesin real-world scenarios. The BEV-based retrieval is trained using the samecontrastive setting and loss as classical retrieval.  Our method C-BEV surpasses the state-of-the-art on the retrieval task onmultiple datasets by a large margin. It is particularly effective inchallenging many-to-one scenarios, e.g. increasing the top-1 recall on VIGOR'scross-area split with unknown orientation from 31.1% to 65.0%. Although themodel is supervised only through a contrastive objective applied on imagepairings, it additionally learns to infer the 3-DoF camera pose on the matchingaerial image, and even yields a lower mean pose error than recent methods thatare explicitly trained with metric groundtruth.

C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation