Syn-Aug: An Effective and General Synchronous Data Augmentation Framework for 3D Object Detection

Abstract

Data augmentation plays an important role in boosting the performance of 3D models, while very few studies handle the 3D point cloud data with this technique. Global augmentation and cut-paste are commonly used augmentation techniques for point clouds, where global augmentation is applied to the entire point cloud of the scene, and cut-paste samples objects from other frames into the current frame. Both types of data augmentation can improve performance, but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling, which may be counterproductive and may hurt the overall performance. In addition, LiDAR is susceptible to signal loss, external occlusion, extreme weather and other factors, which can easily cause object shape changes, while global augmentation and cut-paste cannot effectively enhance the robustness of the model. To this end, we propose Syn-Aug, a synchronous data augmentation framework for LiDAR-based 3D object detection. Specifically, we first propose a novel rendering-based object augmentation technique (Ren-Aug) to enrich training data while enhancing scene realism. Second, we propose a local augmentation technique (Local-Aug) to generate local noise by rotating and scaling objects in the scene while avoiding collisions, which can improve generalisation performance. Finally, we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames. We verify the proposed framework with four different types of 3D object detectors. Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets, proving the effectiveness and generality of Syn-Aug. On KITTI, four different types of baseline models using Syn-Aug improved mAP by 0.89%, 1.35%, 1.61% and 1.14% respectively. On nuScenes, four different types of baseline models using Syn-Aug improved mAP by 14.93%, 10.42%, 8.47% and 6.81% respectively. The code is available at https://github.com/liuhuaijjin/Syn-Aug.