Range And Bird's Eye View Fused Cross-modal Visual Place Recognition

Abstract

Image-to-point cloud cross-modal Visual Place Recognition (VPR) is a challenging task where the query is an RGB image, and the database samples are LiDAR point clouds. Compared to single-modal VPR, this approach benefits from the widespread availability of RGB cameras and the robustness of point clouds in providing accurate spatial geometry and distance information. However, current methods rely on intermediate modalities that capture either the vertical or horizontal field of view, limiting their ability to fully exploit the complementary information from both sensors. In this work, we propose an innovative initial retrieval + re-rank method that effectively combines information from range (or RGB) images and Bird's Eye View (BEV) images. Our approach relies solely on a computationally efficient global descriptor similarity search process to achieve re-ranking. Additionally, we introduce a novel similarity label supervision technique to maximize the utility of limited training data. S

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keypeng2025range

Related papers

• Evaluation Of Visual Place Recognition Methods For Image Pair Retrieval In 3D Vision And Robotics (2026)0.00
• C-BEV: Contrastive Bird's Eye View Training For Cross-view Image Retrieval And 3-dof Pose Estimation (2023)0.00
• Focus On Local: Finding Reliable Discriminative Regions For Visual Place Recognition (2025)10.70
• Embodiedplace: Learning Mixture-of-features With Embodied Constraints For Visual Place Recognition (2025)0.00
• Mixvpr: Feature Mixing For Visual Place Recognition (2023)22.68
• Attention-aware Age-agnostic Visual Place Recognition (2019)8.82
• Lavpr: Benchmarking Language And Vision For Place Recognition (2026)2.35
• Multires-netvlad: Augmenting Place Recognition Training With Low-resolution Imagery (2022)16.01