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

使用鸟瞰图作为嵌入表示的新型可训练的检索体系结构，在交叉视图地理定位任务中表现出色，特别在挑战性的多对一情景中有效，并且能够推断匹配航空图像上的 3 自由度摄像机姿态，甚至比明确通过度量真值进行培训的最新方法具有更低的平均姿态误差。