For 3D object detection, labeling lidar point cloud is difficult, so data
augmentation is an important module to make full use of precious annotated
data. As a widely used data augmentation method, GT-sample effectively improves
detection performance by inserting groundtruths into the lidar frame during
training. However, these samples are often placed in unreasonable areas, which
misleads model to learn the wrong context information between targets and
backgrounds. To address this problem, in this paper, we propose a context-aware
data augmentation method (CA-aug) , which ensures the reasonable placement of
inserted objects by calculating the "Validspace" of the lidar point cloud.
CA-aug is lightweight and compatible with other augmentation methods. Compared
with the GT-sample and the similar method in Lidar-aug(SOTA), it brings higher
accuracy to the existing detectors. We also present an in-depth study of
augmentation methods for the range-view-based(RV-based) models and find that
CA-aug can fully exploit the potential of RV-based networks. The experiment on
KITTI val split shows that CA-aug can improve the mAP of the test model by 8%.Comment: 6 pages, 4 figure