CONTEXT-AWARE DATA AUGMENTATION FOR LIDAR 3D OBJECT DETECTION

For 3D LIDAR object detection, data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-aug effectively improves detection performance by inserting sampled groundtruths into LIDAR frames. However, they are often placed in unreasonable areas, leading to the loss of the semantic information between targets and backgrounds during training. To address this problem, we propose a context-aware data augmentation method (CA-aug), which ensures the proper placement of inserted objects by a simple strategy and produces realistic augmented scenes. CA-aug is lightweight and compatible with other augmentation methods. Experiments conducted on KITTI benckmark show that compared with the GT-aug and the similar method in LIDAR-aug (SOTA), it brings higher accuracy to the existing models especially for the detection of cyclists and perdestrians. We also present an in-depth study of augmentation strategies for the range-view-based (RV-based) models and demonstrate that CA-aug can fully exploit the potential of RV-based networks, boosting the moderate mAP of our test model by 8%.