Given a ground-level query image and a geo-referenced aerial image that
covers the query's local surroundings, fine-grained cross-view localization
aims to estimate the location of the ground camera inside the aerial image.
Recent works have focused on developing advanced networks trained with accurate
ground truth (GT) locations of ground images. However, the trained models
always suffer a performance drop when applied to images in a new target area
that differs from training. In most deployment scenarios, acquiring fine GT,
i.e. accurate GT locations, for target-area images to re-train the network can
be expensive and sometimes infeasible. In contrast, collecting images with
noisy GT with errors of tens of meters is often easy. Motivated by this, our
paper focuses on improving the performance of a trained model in a new target
area by leveraging only the target-area images without fine GT. We propose a
weakly supervised learning approach based on knowledge self-distillation. This
approach uses predictions from a pre-trained model as pseudo GT to supervise a
copy of itself. Our approach includes a mode-based pseudo GT generation for
reducing uncertainty in pseudo GT and an outlier filtering method to remove
unreliable pseudo GT. Our approach is validated using two recent
state-of-the-art models on two benchmarks. The results demonstrate that it
consistently and considerably boosts the localization accuracy in the target
area.

提出了基于知识自蒸馏的弱监督学习方法，用于利用无精细 GT 的目标区域图像来提高模型在新目标区域的性能，包括基于模态的伪 GT 生成和去除不可靠的伪 GT。经验证该方法显著提高了目标区域的定位准确性。

适应没有细粒度地面真实性的领域的细粒度交叉视图定位

Adapting Fine-Grained Cross-View Localization to Areas without Fine  Ground Truth

Image retrieval-based cross-view localization methods often lead to very
coarse camera pose estimation, due to the limited sampling density of the
database satellite images. In this paper, we propose a method to increase the
accuracy of a ground camera's location and orientation by estimating the
relative rotation and translation between the ground-level image and its
matched/retrieved satellite image. Our approach designs a geometry-guided
cross-view transformer that combines the benefits of conventional geometry and
learnable cross-view transformers to map the ground-view observations to an
overhead view. Given the synthesized overhead view and observed satellite
feature maps, we construct a neural pose optimizer with strong global
information embedding ability to estimate the relative rotation between them.
After aligning their rotations, we develop an uncertainty-guided spatial
correlation to generate a probability map of the vehicle locations, from which
the relative translation can be determined. Experimental results demonstrate
that our method significantly outperforms the state-of-the-art. Notably, the
likelihood of restricting the vehicle lateral pose to be within 1m of its
Ground Truth (GT) value on the cross-view KITTI dataset has been improved from
$35.54\%$ to $76.44\%$, and the likelihood of restricting the vehicle
orientation to be within $1^{\circ}$ of its GT value has been improved from
$19.64\%$ to $99.10\%$.

本篇论文提出了一种增加地面摄像头位置和方向准确度的方法，通过估计地平线图像和卫星图像之间的相对旋转和平移，设计一种几何引导的交叉视图转换器，构建一个强大的神经姿态优化器来估计它们之间的相对旋转，通过空间相关性生成车辆位置的概率图，并明显优于现有技术。