  In this work, we shed light on different data augmentation techniques
commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection.
For the bulk of our experiments, we utilize the well known PointPillars
pipeline and the well established KITTI dataset. We investigate a variety of
global and local augmentation techniques, where global augmentation techniques
are applied to the entire point cloud of a scene and local augmentation
techniques are only applied to points belonging to individual objects in the
scene. Our findings show that both types of data augmentation can lead to
performance increases, but it also turns out, that some augmentation
techniques, such as individual object translation, for example, can be
counterproductive and can hurt the overall performance. We show that these
findings transfer and generalize well to other state of the art 3D Object
Detection methods and the challenging STF dataset. On the KITTI dataset we can
gain up to 1.5% and on the STF dataset up to 1.7% in 3D mAP on the moderate car
class.
