  Monocular 3D object detection is a fundamental but very important task to
many applications including autonomous driving, robotic grasping and augmented
reality. Existing leading methods tend to estimate the depth of the input image
first, and detect the 3D object based on point cloud. This routine suffers from
the inherent gap between depth estimation and object detection. Besides, the
prediction error accumulation would also affect the performance. In this paper,
a novel method named MonoSIM is proposed. The insight behind introducing
MonoSIM is that we propose to simulate the feature learning behaviors of a
point cloud based detector for monocular detector during the training period.
Hence, during inference period, the learned features and prediction would be
similar to the point cloud based detector as possible. To achieve it, we
propose one scene-level simulation module, one RoI-level simulation module and
one response-level simulation module, which are progressively used for the
detector's full feature learning and prediction pipeline. We apply our method
to the famous M3D-RPN detector and CaDDN detector, conducting extensive
experiments on KITTI and Waymo Open datasets. Results show that our method
consistently improves the performance of different monocular detectors for a
large margin without changing their network architectures. Our codes will be
publicly available at
https://github.com/sunh18/MonoSIM}{https://github.com/sunh18/MonoSIM.
