基于激光雷达点云的3D目标检测方法综述  

Review of 3D Target Detection Methods Based on LiDAR Point Clouds

作　　者：秦静[1] 王伟滨 邹启杰[2] 汪祖民[2] 季长清[3] QIN Jing;WANG Weibin;ZOU Qijie;WANG Zumin;JI Changqing(College of Software Engineering,Dalian University,Dalian,Liaoning 116622,China;College of Information Engineering,Dalian University,Dalian,Liaoning 116622,China;College of Physical Science and Technology,Dalian University,Dalian,Liaoning 116622,China)

机构地区：[1]大连大学软件工程学院,辽宁大连116622 [2]大连大学信息工程学院,辽宁大连116622 [3]大连大学物理科学与技术学院,辽宁大连116622

出　　处：《计算机科学》2023年第S01期249-255,共7页Computer Science

基　　金：国家自然科学基金青年科学基金项目(62002038)。

摘　　要：近年来,利用激光雷达点云进行3D目标检测是计算机视觉领域的一个研究热点,并在自动驾驶领域备受关注。3D相比于2D而言,结合了深度信息,更能体现出现实世界的特征,以有效解决如路径规划、运动预测、目标检测等方面的实际问题。介绍了3D目标检测的发展背景,概述了基于激光雷达点云数据的3D目标检测框架的流程,比较了几种常见的包含点云信息的数据集,并对主要研究方法进行分类。结合了自动驾驶的应用场景,对不同方法的性能和局限性进行了分析和比较。最后,总结了现阶段的技术难点,并展望了该领域未来的发展前景。In recent years,3D target detection using LiDAR point cloud is a research hotspot in the field of computer vision and has attracted much attention in the field of autonomous driving.Compared with 2D,3D combines depth information to better reflect the characteristics of the real world,to effectively solve practical problems such as path planning,motion prediction,target detection,and other aspects.This paper introduces the development background of 3D target detection,summarizes the flow of 3D target detection framework based on LiDAR point cloud data,compares several common data sets containing point cloud information,and classifies the main research methods.The performance and limitations of different methods are analyzed and compared.Finally,the current technical difficulties are summarized and the future development prospects of this field are forecasted.

关 键 词：目标检测 点云 计算机视觉 激光雷达 多模态融合 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]