DualBEV: Unifying Dual View Transformation with Probabilistic Correspondences
<|reference_start|>DualBEV: Unifying Dual View Transformation with Probabilistic Correspondences: Camera-based Bird's-Eye-View (BEV) perception often struggles between adopting 3D-to-2D or 2D-to-3D view transformation (VT). The 3D-to-2D VT typically employs resource-intensive Transformer to establish robust correspondences between 3D and 2D features, while the 2D-to-3D VT utilizes the Lift-Splat-Shoot (LSS) pipeline for real-time application, potentially missing distant information. To address these limitations, we propose DualBEV, a unified framework that utilizes a shared feature transformation incorporating three probabilistic measurements for both strategies. By considering dual-view correspondences in one stage, DualBEV effectively bridges the gap between these strategies, harnessing their individual strengths. Our method achieves state-of-the-art performance without Transformer, delivering comparable efficiency to the LSS approach, with 55.2% mAP and 63.4% NDS on the nuScenes test set. Code is available at \url{https://github.com/PeidongLi/DualBEV}<|reference_end|>
@article{li2024dualbev:,
    title={DualBEV: Unifying Dual View Transformation with Probabilistic
  Correspondences},
    author={Peidong Li, Wancheng Shen, Qihao Huang, Dixiao Cui},
    journal={arXiv preprint arXiv:2403.05402},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05402},
    primaryClass={cs.CV}
}