Abstract
We propose a novel fine-grained cross-view localization method that estimates the 3 Degrees of Freedom pose of a ground-level image in an aerial image of the surroundings by matching fine-grained features between the two images. The pose is estimated by aligning a point plane generated from the ground image with a point plane sampled from the aerial image. To generate the ground points, we first map ground image features to a 3D point cloud. Our method then learns to select features along the height dimension to pool the 3D points to a Bird's-Eye-View (BEV) plane. This selection enables us to trace which feature in the ground image contributes to the BEV representation. Next, we sample a set of sparse matches from computed point correspondences between the two point planes and compute their relative pose using Procrustes alignment. Compared to the previous state-of-the-art, our method reduces the mean localization error by 28% on the VIGOR cross-area test set. Qualitative results show that our method learns semantically consistent matches across ground and aerial views through weakly supervised learning from the camera pose.
Abstract (translated)
我们提出了一种新颖的细粒度跨视图定位方法，通过匹配两张图像（地面图像和空中俯瞰图像）之间的细粒度特征来估计地面图像在空中图像中的3个自由度姿态。该姿态是通过对生成自地面图像的点平面与从空中图像中采样得到的点平面进行对齐来估算的。 为了生成地面点，我们首先将地面图像的特征映射到一个三维点云上。然后，我们的方法学习在高度维度选择特征以聚合3D点至鸟瞰图（BEV）平面。这种选择使我们能够追踪哪个地面图像中的特征贡献于BEV表示。 接下来，我们在计算出两个点平面之间的对应关系后采样一组稀疏匹配，并使用普罗克鲁斯特斯对齐法计算它们的相对姿态。 与最新的方法相比，在VIGOR跨区域测试集上，我们的方法将平均定位误差降低了28%。定性结果显示，通过从相机姿态中进行弱监督学习，该方法能够学会在地面视图和空中视图之间找到语义一致的匹配关系。
URL
https://arxiv.org/abs/2503.18725
