点云
目标检测
计算机科学
人工智能
水准点(测量)
计算机视觉
激光雷达
组分(热力学)
领域(数学分析)
深度学习
对象(语法)
传感器融合
RGB颜色模型
点(几何)
模式识别(心理学)
遥感
地理
数学分析
物理
数学
几何学
热力学
大地测量学
作者
Zhenming Liang,Yingping Huang
标识
DOI:10.1177/01423312221093147
摘要
Autonomous driving technology has entered into the fast lane of development in recent years. An essential component of autonomous driving technology is scene perception, especially 3D object detection. This work gives a comprehensive survey on the up-to-date deep learning-based approaches for 3D object detection in autonomous driving, and categorizes the existing detection models into three classes in terms of their input data format, including LiDAR point cloud-based, Camera RGB image-based, and LiDAR point cloud-camera image fusion-based 3D object detection methods. This work also discusses and analyzes these models according to their characteristics, basic frameworks, advantages and disadvantages, and exhibits the benchmark datasets which are commonly used in the research community. At last, this work summarizes the review work and provides a discussion on the practical challenges and future trend of the research domain.
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