点云
激光雷达
计算机科学
稳健性(进化)
目标检测
人工智能
对象(语法)
试验数据
点(几何)
计算机视觉
云计算
遥感
模式识别(心理学)
生物化学
基因
操作系统
地质学
数学
化学
程序设计语言
几何学
作者
Junyu Lin,Jiawei Liu,Quanjun Zhang,Xufan Zhang,Chunrong Fang
标识
DOI:10.1145/3510454.3516860
摘要
With the rapid development of object detection in deep learning (DL), applications on LiDAR point clouds have received much attention, such as autonomous driving. To verify the robustness of object detection models by testing, large amounts of diversified annotated LiDAR point clouds are required to be used as test data. However, considering the sparseness of objects, the diversity of the existing point cloud dataset is limited by the number and types of objects. Therefore, it is important to generate diversified point clouds by test data augmentation. In this paper, we propose a tool for LiDAR point cloud via test data augmentation, named TauLiM. A well-designed metamorphic relation (MR) [1] is proposed to augment point clouds while maintaining their physical characteristic of LiDAR. TauLiM is composed of three modules, namely point cloud configuration, coordinate filtering, and object insertion. To evaluate our tool, we employ experiments to compare the ability of testing between the existing dataset and the augmented one. The result shows that TauLiM can effectively augment diversified test data and test the object detection model. The video of TauLiM is available at https://www.youtube.com/watch?v=9S6xpRbbhtQ and TauLiM can be used at http://1.13.193.98:2601/.
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