  Data augmentation has greatly contributed to improving the performance in
image recognition tasks, and a lot of related studies have been conducted.
However, data augmentation on 3D point cloud data has not been much explored.
3D label has more sophisticated and rich structural information than the 2D
label, so it enables more diverse and effective data augmentation. In this
paper, we propose part-aware data augmentation (PA-AUG) that can better utilize
rich information of 3D label to enhance the performance of 3D object detectors.
PA-AUG divides objects into partitions and stochastically applies five
augmentation methods to each local region. It is compatible with existing point
cloud data augmentation methods and can be used universally regardless of the
detector's architecture. PA-AUG has improved the performance of
state-of-the-art 3D object detector for all classes of the KITTI dataset and
has the equivalent effect of increasing the train data by about 2.5$\times$. We
also show that PA-AUG not only increases performance for a given dataset but
also is robust to corrupted data. The code is available at
https://github.com/sky77764/pa-aug.pytorch
