CASIA OpenIR
3D Point Cloud Analysis and Classification in Large-Scale Scene Based on Deep Learning
Wang, Lei1,2,3; Meng, Weiliang4,5; Xi, Runping1,2; Zhang, Yanning1,2; Ma, Chengcheng4,5; Lu, Ling3; Zhang, Xiaopeng4,5
Source PublicationIEEE ACCESS
ISSN2169-3536
2019
Volume7Pages:55649-55658
Corresponding AuthorWang, Lei(wlei598@163.com) ; Xi, Runping(xrp@163.com) ; Zhang, Yanning(ynzhangnpu@qq.com)
AbstractWe 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.
KeywordCNN feature description matrix geometric features point cloud
DOI10.1109/ACCESS.2019.2909742
WOS KeywordMULTISCALE
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000467988500001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24207
Collection中国科学院自动化研究所
Corresponding AuthorWang, Lei; Xi, Runping; Zhang, Yanning
Affiliation1.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
Recommended Citation
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.
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