论文标题
量化基于LIDAR的3D对象检测的数据增强
Quantifying Data Augmentation for LiDAR based 3D Object Detection
论文作者
论文摘要
在这项工作中，我们阐明了基于光检测和范围（LIDAR）3D对象检测的不同数据增强技术。对于我们的大部分实验，我们利用众所周知的Pointpillars管道和已建立的Kitti数据集。我们研究了各种全球和局部增强技术，其中将全球增强技术应用于场景的整个点云，而局部增强技术仅应用于场景中属于单个对象的点。我们的发现表明，两种类型的数据增强都可以导致性能提高，但事实证明，某些增强技术（例如单个对象翻译）例如，可以适得其反，并可能损害整体性能。我们表明，这些发现转移并概括到其他最先进的3D对象检测方法和具有挑战性的STF数据集。在KITTI数据集上，我们可以在中等汽车类的3D地图中最多可获得1.5％，而在STF数据集上最多可获得1.7％。
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.
