Point Cloud Processing Methods for 3D Point Cloud Detection Tasks  

作　　者：WANG Chongchong LI Yao WANG Beibei CAO Hong ZHANG Yanyong 

机构地区：[1]Anhui University,Hefei 230601,China [2]University of Science and Technology of China,Hefei 230026,China [3]Institute of Artificial Intelligence,Hefei Comprehensive National Sci-ence Center,Hefei 230026,China

出　　处：《ZTE Communications》2023年第4期38-46,共9页中兴通讯技术（英文版）

摘　　要：Light detection and ranging(LiDAR)sensors play a vital role in acquiring 3D point cloud data and extracting valuable information about objects for tasks such as autonomous driving,robotics,and virtual reality(VR).However,the sparse and disordered nature of the 3D point cloud poses significant challenges to feature extraction.Overcoming limitations is critical for 3D point cloud processing.3D point cloud object detection is a very challenging and crucial task,in which point cloud processing and feature extraction methods play a crucial role and have a significant impact on subsequent object detection performance.In this overview of outstanding work in object detection from the 3D point cloud,we specifically focus on summarizing methods employed in 3D point cloud processing.We introduce the way point clouds are processed in classical 3D object detection algorithms,and their improvements to solve the problems existing in point cloud processing.Different voxelization methods and point cloud sampling strategies will influence the extracted features,thereby impacting the final detection performance.

关 键 词：point cloud processing 3D object detection point cloud voxelization bird's eye view deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]