  Data and model are the undoubtable two supporting pillars for LiDAR object
detection. However, data-centric works have fallen far behind compared with the
ever-growing list of fancy new models. In this work, we systematically study
the synthesis-based LiDAR data augmentation approach (so-called GT-Aug) which
offers maxium controllability over generated data samples. We pinpoint the main
shortcoming of existing works is introducing unrealistic LiDAR scan patterns
during GT-Aug. In light of this finding, we propose Real-Aug, a synthesis-based
augmentation method which prioritizes on generating realistic LiDAR scans. Our
method consists a reality-conforming scene composition module which handles the
details of the composition and a real-synthesis mixing up training strategy
which gradually adapts the data distribution from synthetic data to the real
one. To verify the effectiveness of our methods, we conduct extensive ablation
studies and validate the proposed Real-Aug on a wide combination of detectors
and datasets. We achieve a state-of-the-art 0.744 NDS and 0.702 mAP on nuScenes
test set. The code shall be released soon.
