CrossBEV-PR: Cross-modal Visual-LiDAR Place Recognition via BEV Feature Distillation

Abstract

Utilizing 2D images for place recognition within 3D point cloud maps presents significant challenges in autonomous driving applications, primarily due to the inherent cross-modal disparity between visual and LiDAR data. In this study, we propose a novel cross-modal visual-LiDAR place recognition method based on Bird’s Eye View (BEV) feature distillation. Our framework is the first end-to-end solution designed to achieve cross-modal place recognition between surround-view images and LiDAR point clouds. By encoding features into a unified BEV representation, our approach effectively bridges the modality gap between 3D and 2D data. Additionally, we introduce a teacher-student distillation training strategy to further enhance the network’s cross-modal generalization capabilities. Extensive experiments on benchmark datasets, including nuScenes and Argoverse, demonstrate that our method achieves state-of-the-art (SOTA) performance in cross-modal place recognition tasks. Furthermore, validation on the SJTU-Sanya dataset confirms the robustness and adaptability of our approach in real-world scenarios. We publicly release our network model and implementation details at https://github.com/IRMVLab/CrossBEV-PR.