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.

翻译：本文针对地面图像相对于包含局部环境的卫星图像进行3自由度相机位姿估计的问题。我们提出了一种新颖的端到端方法，通过学习地面与卫星图像对中密集像素级流场来计算相机位姿。与现有方法不同，我们的方法在像素级别构建特征度量，从而实现对全图像的监督，以学习跨视角的独特几何结构与视觉外观。具体而言，该方法采用两个不同的卷积网络分别提取地面与卫星特征。随后，我们利用固定相机高度假设将地面特征图投影至鸟瞰视角（BEV），实现初步几何对齐。为进一步建立BEV与卫星特征之间的内容关联，我们引入残差卷积模块对投影后的BEV特征进行细化。基于RAFT的流解码器网络对细化后的BEV特征图和卫星特征图进行光流估计。在获得密集流对应关系后，我们采用最小二乘法筛选匹配内点并回归地面相机位姿。大量实验表明，与现有最优方法相比，我们的方法取得了显著改进。值得注意的是，本方法在KITTI、Ford multi-AV、VIGOR和Oxford RobotCar数据集上分别将中值定位误差降低了89%、19%、80%和35%。