  Fusing the camera and LiDAR information has become a de-facto standard for 3D
object detection tasks. Current methods rely on point clouds from the LiDAR
sensor as queries to leverage the feature from the image space. However, people
discovered that this underlying assumption makes the current fusion framework
infeasible to produce any prediction when there is a LiDAR malfunction,
regardless of minor or major. This fundamentally limits the deployment
capability to realistic autonomous driving scenarios. In contrast, we propose a
surprisingly simple yet novel fusion framework, dubbed BEVFusion, whose camera
stream does not depend on the input of LiDAR data, thus addressing the downside
of previous methods. We empirically show that our framework surpasses the
state-of-the-art methods under the normal training settings. Under the
robustness training settings that simulate various LiDAR malfunctions, our
framework significantly surpasses the state-of-the-art methods by 15.7% to
28.9% mAP. To the best of our knowledge, we are the first to handle realistic
LiDAR malfunction and can be deployed to realistic scenarios without any
post-processing procedure. The code is available at
https://github.com/ADLab-AutoDrive/BEVFusion.
