This paper addresses the problem of estimating the 3-DoF camera pose for a
ground-level image with respect to a satellite image that encompasses the local
surroundings. We propose a novel end-to-end approach that leverages the
learning of dense pixel-wise flow fields in pairs of ground and satellite
images to calculate the camera pose. Our approach differs from existing methods
by constructing the feature metric at the pixel level, enabling full-image
supervision for learning distinctive geometric configurations and visual
appearances across views. Specifically, our method employs two distinct
convolution networks for ground and satellite feature extraction. Then, we
project the ground feature map to the bird's eye view (BEV) using a fixed
camera height assumption to achieve preliminary geometric alignment. To further
establish content association between the BEV and satellite features, we
introduce a residual convolution block to refine the projected BEV feature.
Optical flow estimation is performed on the refined BEV feature map and the
satellite feature map using flow decoder networks based on RAFT. After
obtaining dense flow correspondences, we apply the least square method to
filter matching inliers and regress the ground camera pose. Extensive
experiments demonstrate significant improvements compared to state-of-the-art
methods. Notably, our approach reduces the median localization error by 89%,
19%, 80% and 35% on the KITTI, Ford multi-AV, VIGOR and Oxford RobotCar
datasets, respectively.

通过学习地面和卫星图像中稠密的像素级光流场来计算相机姿态，本文提出了一种新的端到端方法，该方法与现有方法不同之处在于通过在像素级构建特征度量，从而实现全图监督学习从而获取地面和卫星图像的独特的几何配置和视觉外貌。经过广泛的实验，证明了与最新的方法相比，在 KITTI、Ford multi-AV、VIGOR 和 Oxford RobotCar 数据集上，我们的方法将中位数定位误差分别降低了 89%、19%、80% 和 35%。