论文标题
LIDAR 3D对象检测的上下文感知数据增强
Context-Aware Data Augmentation for LIDAR 3D Object Detection
论文作者
论文摘要
对于3D对象检测，很难将LiDAR点云标记，因此数据增加是充分利用宝贵注释数据的重要模块。作为一种广泛使用的数据增强方法，GT样本在训练过程中通过将地面确实插入激光雷达框架中有效地提高了检测性能。但是，这些样本通常被放置在不合理的区域，这些区域误导了模型，以学习目标和背景之间的错误上下文信息。为了解决这个问题，在本文中，我们提出了一种上下文感知的数据增强方法（CA-AUG），该方法通过计算LiDAR Point Cloud的“有效空间”来确保插入对象的合理放置。 Ca-aug轻巧，与其他增强方法兼容。与LIDAR-AUG（SOTA）中的GT样本和类似方法相比，它为现有检测器带来了更高的准确性。我们还对基于范围的基于范围（RV）模型的增强方法进行了深入研究，并发现CA-AUG可以完全利用基于RV的网络的潜力。 Kitti Val拆分的实验表明，CA-EAG可以将测试模型的图提高8％。
For 3D object detection, labeling lidar point cloud is difficult, so data augmentation is an important module to make full use of precious annotated data. As a widely used data augmentation method, GT-sample effectively improves detection performance by inserting groundtruths into the lidar frame during training. However, these samples are often placed in unreasonable areas, which misleads model to learn the wrong context information between targets and backgrounds. To address this problem, in this paper, we propose a context-aware data augmentation method (CA-aug) , which ensures the reasonable placement of inserted objects by calculating the "Validspace" of the lidar point cloud. CA-aug is lightweight and compatible with other augmentation methods. Compared with the GT-sample and the similar method in Lidar-aug(SOTA), it brings higher accuracy to the existing detectors. We also present an in-depth study of augmentation methods for the range-view-based(RV-based) models and find that CA-aug can fully exploit the potential of RV-based networks. The experiment on KITTI val split shows that CA-aug can improve the mAP of the test model by 8%.
