Focusing on Projection-Stable Patch: Cross-View Localization with Geometric-Semantic Alignment

Abstract

This paper presents a novel feature alignment strategy for cross-view geo-localization to bridge the perspective gap between ground and satellite images. Existing methods for cross-view geo-localization often overlook factors such as occlusion and distortion errors caused by viewpoint transformation. These issues lead to reduced accuracy in complex scenes. To address this issue, we propose a framework comprising two novel components: a perspective-driven attention fusion (PDAF) module that aligns ground and satellite features through cross-view semantic correlation, effectively preserving structural consistency during view transformation; and a projection-stable patch-guided pose optimizer (PSPG) that enhances geometric reliability by selectively focusing on projection-stable patch to refine pose estimation. The PDAF module mitigates information loss through attention fusion between ground and bird’s-eye-view (BEV) feature maps representations, while the PSPG refines pose estimation by dynamically suppressing unstable features through geometrically unstable token merging. Comprehensive evaluations on KITTI and Ford Multi-AV datasets demonstrate our method’s superiority in orientation estimation and competitive location accuracy compared to state-of-the-art approaches. Qualitative results further confirm the framework’s robustness in complex localization scenarios. The code is available at https://github.com/RobVisLab-NJUST/CVLGSA