论文标题
在点云中3D对象检测的零件感知数据增强
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
论文作者
论文摘要
数据增强极大地有助于提高图像识别任务的性能，并进行了许多相关研究。但是，在3D点云数据上的数据增强尚未得到太多探索。与2D标签相比，3D标签具有更复杂和丰富的结构信息，因此它可以实现更多多样化和有效的数据增强。在本文中，我们提出了可以更好地利用3D标签信息来增强3D对象检测器的性能的零件感知数据增强（PA-AUG）。 Pa-aug将对象分为分区，然后随机地将五种增强方法应用于每个局部区域。它与现有的点云数据增强方法兼容，无论检测器的体系结构如何，都可以普遍使用。 Pa-aug改善了对Kitti数据集的所有类别的最先进的3D对象检测器的性能，并且具有将火车数据增加约2.5 $ \ times $的等效效果。我们还表明，PA-aug不仅提高了给定数据集的性能，而且对损坏的数据也很强。该代码可从https://github.com/sky77764/pa-aug.pytorch获得
Data augmentation has greatly contributed to improving the performance in image recognition tasks, and a lot of related studies have been conducted. However, data augmentation on 3D point cloud data has not been much explored. 3D label has more sophisticated and rich structural information than the 2D label, so it enables more diverse and effective data augmentation. In this paper, we propose part-aware data augmentation (PA-AUG) that can better utilize rich information of 3D label to enhance the performance of 3D object detectors. PA-AUG divides objects into partitions and stochastically applies five augmentation methods to each local region. It is compatible with existing point cloud data augmentation methods and can be used universally regardless of the detector's architecture. PA-AUG has improved the performance of state-of-the-art 3D object detector for all classes of the KITTI dataset and has the equivalent effect of increasing the train data by about 2.5$\times$. We also show that PA-AUG not only increases performance for a given dataset but also is robust to corrupted data. The code is available at https://github.com/sky77764/pa-aug.pytorch
