Deep Learning on Point Clouds and Its Application: A Survey

doi:10.3390/s19194188

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 智能机器人系统研究

	Deep Learning on Point Clouds and Its Application: A Survey
	Liu, Weiping 1; Sun, Jia2 ; Li, Wanyi2 ; Hu, Ting 1; Wang, Peng2
发表期刊	SENSORS
	2019-10-01
卷号	19 期号:19 页码:22
通讯作者	Li, Wanyi(wanyi.li@ia.ac.cn) ; Hu, Ting(tinghu@whu.edu.cn)
摘要	Point cloud is a widely used 3D data form, which can be produced by depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. Being unordered and irregular, many researchers focused on the feature engineering of the point cloud. Being able to learn complex hierarchical structures, deep learning has achieved great success with images from cameras. Recently, many researchers have adapted it into the applications of the point cloud. In this paper, the recent existing point cloud feature learning methods are classified as point-based and tree-based. The former directly takes the raw point cloud as the input for deep learning. The latter first employs a k-dimensional tree (Kd-tree) structure to represent the point cloud with a regular representation and then feeds these representations into deep learning models. Their advantages and disadvantages are analyzed. The applications related to point cloud feature learning, including 3D object classification, semantic segmentation, and 3D object detection, are introduced, and the datasets and evaluation metrics are also collected. Finally, the future research trend is predicted.
关键词	feature learning deep learning point cloud application of point cloud
DOI	10.3390/s19194188
关键词[WOS]	OBJECT RECOGNITION ; SEGMENTATION ; CLASSIFICATION
收录类别	SCI
语种	英语
资助项目	Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[2015112] ; China Postdoctoral Science Foundation[2018M641523] ; National Key Research and Development Plan of China[2017YFB1300202] ; National Natural Science Foundation of China[U1613213] ; National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[61771471] ; National Natural Science Foundation of China[61771471] ; National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[U1613213] ; National Key Research and Development Plan of China[2017YFB1300202] ; China Postdoctoral Science Foundation[2018M641523] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[2015112]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Plan of China ; China Postdoctoral Science Foundation ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000494823200132
出版者	MDPI
七大方向——子方向分类	目标检测、跟踪与识别
引用统计	被引频次：133[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28915
专题	多模态人工智能系统全国重点实验室_智能机器人系统研究
通讯作者	Li, Wanyi; Hu, Ting
作者单位	1.Wuhan Univ, Sch Math & Stat, Wuhan 430072, Hubei, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu, Weiping,Sun, Jia,Li, Wanyi,et al. Deep Learning on Point Clouds and Its Application: A Survey[J]. SENSORS,2019,19(19):22.
APA	Liu, Weiping,Sun, Jia,Li, Wanyi,Hu, Ting,&Wang, Peng.(2019).Deep Learning on Point Clouds and Its Application: A Survey.SENSORS,19(19),22.
MLA	Liu, Weiping,et al."Deep Learning on Point Clouds and Its Application: A Survey".SENSORS 19.19(2019):22.