通过细粒度特征匹配实现地面与航拍图像的精准定位
FG$^2$: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching
- 将地面图像特征映射为3D点云，筛选高度维度特征生成鸟瞰图平面
- 在跨视图点对应关系中采样稀疏匹配，用普鲁克斯特对齐计算姿态
- 在VIGOR数据集上定位误差降低28%，适合弱监督下的跨视角定位
我们提出一种新型细粒度跨视图定位方法，通过匹配地面图像与航拍图像之间的细粒度特征，估计地面图像在周围环境航拍图像中的3自由度姿态。该方法通过将地面图像特征映射至3D点云，并学习沿高度维度选择特征，将3D点云池化为鸟瞰图（BEV）平面，从而追踪每个地表特征对鸟瞰表示的贡献。随后，从两平面点对应关系中采样一组稀疏匹配，并使用普鲁克斯特对齐算法计算其相对姿态。相比现有最优方法，本方法在VIGOR跨区域测试集上的平均定位误差降低了28%。定性结果表明，该方法通过相机姿态的弱监督学习，实现了地面与航拍视图间的语义一致匹配。
原文摘要 · Abstract (English)
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.
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