  In autonomous driving, data augmentation is commonly used for improving 3D
object detection. The most basic methods include insertion of copied objects
and rotation and scaling of the entire training frame. Numerous variants have
been developed as well. The existing methods, however, are considerably limited
when compared to the variety of the real world possibilities. In this work, we
develop a diversified and realistic augmentation method that can flexibly
construct a whole-body object, freely locate and rotate the object, and apply
self-occlusion and external-occlusion accordingly. To improve the diversity of
the whole-body object construction, we develop an iterative method that
stochastically combines multiple objects observed from the real world into a
single object. Unlike the existing augmentation methods, the constructed
objects can be randomly located and rotated in the training frame because
proper occlusions can be reflected to the whole-body objects in the final step.
Finally, proper self-occlusion at each local object level and
external-occlusion at the global frame level are applied using the Hidden Point
Removal (HPR) algorithm that is computationally efficient. HPR is also used for
adaptively controlling the point density of each object according to the
object's distance from the LiDAR. Experiment results show that the proposed
DR.CPO algorithm is data-efficient and model-agnostic without incurring any
computational overhead. Also, DR.CPO can improve mAP performance by 2.08% when
compared to the best 3D detection result known for KITTI dataset. The code is
available at https://github.com/SNU-DRL/DRCPO.git
