In this paper, we introduce a novel approach to fine-grained cross-view
geo-localization. Our method aligns a warped ground image with a corresponding
GPS-tagged satellite image covering the same area using homography estimation.
We first employ a differentiable spherical transform, adhering to geometric
principles, to accurately align the perspective of the ground image with the
satellite map. This transformation effectively places ground and aerial images
in the same view and on the same plane, reducing the task to an image alignment
problem. To address challenges such as occlusion, small overlapping range, and
seasonal variations, we propose a robust correlation-aware homography estimator
to align similar parts of the transformed ground image with the satellite
image. Our method achieves sub-pixel resolution and meter-level GPS accuracy by
mapping the center point of the transformed ground image to the satellite image
using a homography matrix and determining the orientation of the ground camera
using a point above the central axis. Operating at a speed of 30 FPS, our
method outperforms state-of-the-art techniques, reducing the mean metric
localization error by 21.3% and 32.4% in same-area and cross-area
generalization tasks on the VIGOR benchmark, respectively, and by 34.4% on the
KITTI benchmark in same-area evaluation.

我们提出一种新颖的方法来实现细粒度的跨视图地理定位，通过使用单应性估计将畸变的地面图像与相同区域的带 GPS 标记的卫星图像对齐，将地面和航拍图像置于同一视图和同一平面上，减少了任务的复杂性，通过映射变换后地面图像的中心点到卫星图像并确定地面相机的方向来实现亚像素和米级的 GPS 精度定位。