Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object
  Detection
Junjie Huang Yun Ye Zhujin Liang Yi Shan Dalong Du
3D object Detection with LiDAR-camera encounters overfitting in algorithm
development which is derived from the violation of some fundamental rules. We
refer to the data annotation in dataset construction for theory complementing
and argue that the regression task prediction should not involve the feature
from the camera branch. By following the cutting-edge perspective of 'Detecting
As Labeling', we propose a novel paradigm dubbed DAL. With the most classical
elementary algorithms, a simple predicting pipeline is constructed by imitating
the data annotation process. Then we train it in the simplest way to minimize
its dependency and strengthen its portability. Though simple in construction
and training, the proposed DAL paradigm not only substantially pushes the
performance boundary but also provides a superior trade-off between speed and
accuracy among all existing methods. With comprehensive superiority, DAL is an
ideal baseline for both future work development and practical deployment. The
code has been released to facilitate future work on
https://github.com/HuangJunJie2017/BEVDet.