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.

本文研究了 LiDAR 技术的数据增强技术，探讨了全局和局部增强技术的不同使用方式，通过在 KITTI 数据集上的实验表明了两种数据增强技术都有可能促进 3D 目标检测方法的性能，但个别增强技术例如物体平移对全局性能有负面影响，作者认为这些结论同样适用于其他最前沿的 3D 目标检测方法，并在 STF 数据集上验证了实验结果，最大提升了 1.7% 的 3D mAP。