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

TL;DR

BEVDet introduces a bird's-eye-view paradigm for 3D object detection in autonomous driving, achieving high accuracy and efficiency by reusing modules and employing novel data augmentation and NMS strategies.

Contribution

This paper presents the BEVDet framework, a scalable and effective approach for 3D object detection in BEV, with significant performance improvements over existing methods.

Findings

BEVDet-Tiny achieves 31.2% mAP and 39.2% NDS at 15.6 FPS.

BEVDet-Base scores 39.3% mAP and 47.2% NDS, surpassing previous results.

The method is computationally efficient, requiring only 11% of the GFLOPs of comparable models.

Abstract

Autonomous driving perceives its surroundings for decision making, which is one of the most complex scenarios in visual perception. The success of paradigm innovation in solving the 2D object detection task inspires us to seek an elegant, feasible, and scalable paradigm for fundamentally pushing the performance boundary in this area. To this end, we contribute the BEVDet paradigm in this paper. BEVDet performs 3D object detection in Bird-Eye-View (BEV), where most target values are defined and route planning can be handily performed. We merely reuse existing modules to build its framework but substantially develop its performance by constructing an exclusive data augmentation strategy and upgrading the Non-Maximum Suppression strategy. In the experiment, BEVDet offers an excellent trade-off between accuracy and time-efficiency. As a fast version, BEVDet-Tiny scores 31.2% mAP and 39.2% NDS on 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 inference speed, it surpasses FCOS3D by a large margin of +9.8% mAP and +10.0% NDS. The source code is publicly available for further research at https://github.com/HuangJunJie2017/BEVDet .