DualBEV: CNN is All You Need in View Transformation

• URL: http://arxiv.org/abs/2403.05402v1
• Date: Fri, 8 Mar 2024 15:58:00 GMT
• Title: DualBEV: CNN is All You Need in View Transformation
• Authors: Peidong Li, Wancheng Shen, Qihao Huang and Dixiao Cui
• Abstract summary: Camera-based Bird's-Eye-View (BEV) perception often struggles between adopting 3D-to-2D or 2D-to-3D view transformation (VT) We propose DualBEV, a unified framework that utilizes a shared CNN-based feature transformation three probabilistic measurements for both strategies. 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.
• Score: 0.032771631221674334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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