3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning

doi:10.1109/ACCESS.2019.2909742

	3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning
	Wang, Lei 1,2,3; Meng, Weiliang4,5 ; Xi, Runping 1,2; Zhang, Yanning 1,2; Ma, Chengcheng 4,5; Lu, Ling 3; Zhang, Xiaopeng4,5
刊名	IEEE ACCESS
	2019
卷号	7 页码:55649-55658
关键词	CNN feature description matrix geometric features point cloud
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2909742
通讯作者	Wang, Lei(wlei598@163.com) ; Xi, Runping(xrp@163.com) ; Zhang, Yanning(ynzhangnpu@qq.com)
英文摘要	We present a deep learning framework for efficient large-scale 3D point cloud analysis and classification using the designed feature description matrix (FDM). As the 3D points are unordered in the large-scale scene, and no topology structure can be employed directly for classification and recognition, it is difficult to apply deep neural network directly on 3D point clouds as points cannot be arranged in a fixed order as 2D image pixels. We design a new pipeline for 3D data processing by combining the traditional features extraction method and deep learning method. Our FDM encapsulates the 3D features of the point and can be used as the input of the deep neural network for training and testing. The experiments demonstrate that our method can acquire higher classification accuracy compared with our previous work and other state-of-art works.
资助项目	National Natural Science Foundation of China[61561003] ; National Natural Science Foundation of China[61571439] ; National Natural Science Foundation of China[61572405] ; National Natural Science Foundation of China[61761003] ; National Natural Science Foundation of China[61571046] ; Beijing Natural Science Foundation[4184102] ; Beijing Natural Science Foundation[L182059]
WOS关键词	MULTISCALE
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000467988500001
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24206]
专题	中国科学院自动化研究所
通讯作者	Wang, Lei; Xi, Runping; Zhang, Yanning
作者单位	1.Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Shaanxi, Peoples R China 2.Natl Engn Lab Integrated Aerospace Ground Ocean B, Xian 710072, Shaanxi, Peoples R China 3.East China Univ Technol, Jiangxi Engn Lab Radioact Geosci & Big Data Techn, Nanchang 330013, Jiangxi, Peoples R China 4.CAS Inst Automat, LIAMA NLPR, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Wang, Lei,Meng, Weiliang,Xi, Runping,et al. 3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning[J]. IEEE ACCESS,2019,7:55649-55658.
APA	Wang, Lei.,Meng, Weiliang.,Xi, Runping.,Zhang, Yanning.,Ma, Chengcheng.,...&Zhang, Xiaopeng.(2019).3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning.IEEE ACCESS,7,55649-55658.
MLA	Wang, Lei,et al."3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning".IEEE ACCESS 7(2019):55649-55658.