  Monocular 3D object detection aims to localize 3D bounding boxes in an input
single 2D image. It is a highly challenging problem and remains open,
especially when no extra information (e.g., depth, lidar and/or multi-frames)
can be leveraged in training and/or inference. This paper proposes a simple yet
effective formulation for monocular 3D object detection without exploiting any
extra information. It presents the MonoCon method which learns Monocular
Contexts, as auxiliary tasks in training, to help monocular 3D object
detection. The key idea is that with the annotated 3D bounding boxes of objects
in an image, there is a rich set of well-posed projected 2D supervision signals
available in training, such as the projected corner keypoints and their
associated offset vectors with respect to the center of 2D bounding box, which
should be exploited as auxiliary tasks in training. The proposed MonoCon is
motivated by the Cramer-Wold theorem in measure theory at a high level. In
implementation, it utilizes a very simple end-to-end design to justify the
effectiveness of learning auxiliary monocular contexts, which consists of three
components: a Deep Neural Network (DNN) based feature backbone, a number of
regression head branches for learning the essential parameters used in the 3D
bounding box prediction, and a number of regression head branches for learning
auxiliary contexts. After training, the auxiliary context regression branches
are discarded for better inference efficiency. In experiments, the proposed
MonoCon is tested in the KITTI benchmark (car, pedestrain and cyclist). It
outperforms all prior arts in the leaderboard on car category and obtains
comparable performance on pedestrian and cyclist in terms of accuracy. Thanks
to the simple design, the proposed MonoCon method obtains the fastest inference
speed with 38.7 fps in comparisons
