In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. For the bulk of our experiments, we utilize the well known PointPillars pipeline and the well established KITTI dataset. We investigate a variety of global and local augmentation techniques, where global augmentation techniques are applied to the entire point cloud of a scene and local augmentation techniques are only applied to points belonging to individual objects in the scene. Our findings show that both types of data augmentation can lead to performance increases, but it also turns out, that some augmentation techniques, such as individual object translation, for example, can be counterproductive and can hurt the overall performance. We show that these findings transfer and generalize well to other state of the art 3D Object Detection methods and the challenging STF dataset. On the KITTI dataset we can gain up to 1.5% and on the STF dataset up to 1.7% in 3D mAP on the moderate car class.
翻译:在这项工作中,我们揭示了光探测和测距(LiDAR)基于 3D 对象探测(LiDAR) 的光探测和测距(LiDAR) 中常用的不同数据增强技术。 在大部分实验中,我们使用了众所周知的PointPillars输油管和公认的KITTI数据集。我们调查了各种全球和地方增强技术,这些技术适用于场景的整个点云,而地方增强技术只适用于现场个别物体的点。我们的调查结果显示,两种数据增强都可能导致性能提高,但也证明,某些增强技术,例如单个对象翻译,可能起反作用,并可能损害整个性能。我们显示,这些结果转移了第3D 对象探测方法和具有挑战性的STF数据集的其他状态,并在KITTI数据集中,我们可以在中度汽车级的3D mAP中获取1.5%的STF数据集,达到1.7%。