PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion
<|reference_start|>PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with Image and Point Cloud Fusion: Place recognition plays a crucial role in the fields of robotics and computer vision, finding applications in areas such as autonomous driving, mapping, and localization. Place recognition identifies a place using query sensor data and a known database. One of the main challenges is to develop a model that can deliver accurate results while being robust to environmental variations. We propose two multi-modal place recognition models, namely PRFusion and PRFusion++. PRFusion utilizes global fusion with manifold metric attention, enabling effective interaction between features without requiring camera-LiDAR extrinsic calibrations. In contrast, PRFusion++ assumes the availability of extrinsic calibrations and leverages pixel-point correspondences to enhance feature learning on local windows. Additionally, both models incorporate neural diffusion layers, which enable reliable operation even in challenging environments. We verify the state-of-the-art performance of both models on three large-scale benchmarks. Notably, they outperform existing models by a substantial margin of +3.0 AR@1 on the demanding Boreas dataset. Furthermore, we conduct ablation studies to validate the effectiveness of our proposed methods. The codes are available at: https://github.com/sijieaaa/PRFusion<|reference_end|>
@article{wang2024prfusion:,
    title={PRFusion: Toward Effective and Robust Multi-Modal Place Recognition with
  Image and Point Cloud Fusion},
    author={Sijie Wang, Qiyu Kang, Rui She, Kai Zhao, Yang Song, Wee Peng Tay},
    journal={arXiv preprint arXiv:2410.04939},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04939},
    primaryClass={cs.CV}
}