Simple-BEV: What Really Matters for Multi-Sensor BEV Perception?

• Published in: 2023 IEEE International Conference on Robotics and Automation (ICRA) pp. 2759 - 2765
• Main Authors: Harley, Adam W., Fang, Zhaoyuan, Li, Jie, Ambrus, Rares, Fragkiadaki, Katerina
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.05.2023
• Subjects: Buildings; Cameras; Laser radar; Protocols; Space vehicles; Three-dimensional displays; Training
• DOI: 10.1109/ICRA48891.2023.10160831

Building 3D perception systems for autonomous vehicles that do not rely on high-density LiDAR is a critical research problem because of the expense of LiDAR systems compared to cameras and other sensors. Recent research has developed a variety of camera-only methods, where features are differentiably "lifted" from the multi-camera images onto the 2D ground plane, yielding a "bird's eye view" (BEV) feature representation of the 3D space around the vehicle. This line of work has produced a variety of novel "lifting" methods, but we observe that other details in the training setups have shifted at the same time, making it unclear what really matters in top-performing methods. We also observe that using cameras alone is not a real-world constraint, considering that additional sensors like radar have been integrated into real vehicles for years already. In this paper, we first of all attempt to elucidate the high-impact factors in the design and training protocol of BEV perception models. We find that batch size and input resolution greatly affect performance, while lifting strategies have a more modest effect-even a simple parameter-free lifter works well. Second, we demonstrate that radar data can provide a substantial boost to performance, helping to close the gap between camera-only and LiDAR-enabled systems. We analyze the radar usage details that lead to good performance, and invite the community to re-consider this commonly-neglected part of the sensor platform.