  Monocular 3D object detection has become a mainstream approach in automatic
driving for its easy application. A prominent advantage is that it does not
need LiDAR point clouds during the inference. However, most current methods
still rely on 3D point cloud data for labeling the ground truths used in the
training phase. This inconsistency between the training and inference makes it
hard to utilize the large-scale feedback data and increases the data collection
expenses. To bridge this gap, we propose a new weakly supervised monocular 3D
objection detection method, which can train the model with only 2D labels
marked on images. To be specific, we explore three types of consistency in this
task, i.e. the projection, multi-view and direction consistency, and design a
weakly-supervised architecture based on these consistencies. Moreover, we
propose a new 2D direction labeling method in this task to guide the model for
accurate rotation direction prediction. Experiments show that our
weakly-supervised method achieves comparable performance with some fully
supervised methods. When used as a pre-training method, our model can
significantly outperform the corresponding fully-supervised baseline with only
1/3 3D labels. https://github.com/weakmono3d/weakmono3d
