  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 feature, 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 CNN-based 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 will be released at
https://github.com/PeidongLi/DualBEV.
