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Autonomous driving perceives its surroundings for decision making, which isone of the most complex scenarios in visual perception. The success of paradigminnovation in solving the 2D object detection task inspires us to seek anelegant, feasible, and scalable paradigm for fundamentally pushing theperformance boundary in this area. To this end, we contribute the BEVDetparadigm in this paper. BEVDet performs 3D object detection in Bird-Eye-View(BEV), where most target values are defined and route planning can be handilyperformed. We merely reuse existing modules to build its framework butsubstantially develop its performance by constructing an exclusive dataaugmentation strategy and upgrading the Non-Maximum Suppression strategy. Inthe experiment, BEVDet offers an excellent trade-off between accuracy andtime-efficiency. As a fast version, BEVDet-Tiny scores 31.2% mAP and 39.2% NDSon the nuScenes val set. It is comparable with FCOS3D, but requires just 11%computational budget of 215.3 GFLOPs and runs 9.2 times faster at 15.6 FPS.Another high-precision version dubbed BEVDet-Base scores 39.3% mAP and 47.2%NDS, significantly exceeding all published results. With a comparable inferencespeed, it surpasses FCOS3D by a large margin of +9.8% mAP and +10.0% NDS. Thesource code is publicly available for further research athttps://github.com/HuangJunJie2017/BEVDet .

BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View