MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy di...

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Autores principales:	Shijie Su, Chao Wang, Ke Chen, Jian Zhang, Hui Yang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	point cloud registration template point cloud multiple partial point cloud deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/63d4eee0502d4cc19005343ef53e2046
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id	oai:doaj.org-article:63d4eee0502d4cc19005343ef53e2046
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spelling	oai:doaj.org-article:63d4eee0502d4cc19005343ef53e20462021-11-25T16:30:57ZMPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template10.3390/app1122105352076-3417https://doaj.org/article/63d4eee0502d4cc19005343ef53e20462021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10535https://doaj.org/toc/2076-3417With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain the final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.Shijie SuChao WangKe ChenJian ZhangHui YangMDPI AGarticlepoint cloud registrationtemplate point cloudmultiple partial point clouddeep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10535, p 10535 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	point cloud registration template point cloud multiple partial point cloud deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	point cloud registration template point cloud multiple partial point cloud deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Shijie Su Chao Wang Ke Chen Jian Zhang Hui Yang MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
description	With advancements in photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually being applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain the final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.
format	article
author	Shijie Su Chao Wang Ke Chen Jian Zhang Hui Yang
author_facet	Shijie Su Chao Wang Ke Chen Jian Zhang Hui Yang
author_sort	Shijie Su
title	MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
title_short	MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
title_full	MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
title_fullStr	MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
title_full_unstemmed	MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
title_sort	mpcr-net: multiple partial point clouds registration network using a global template
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/63d4eee0502d4cc19005343ef53e2046
work_keys_str_mv	AT shijiesu mpcrnetmultiplepartialpointcloudsregistrationnetworkusingaglobaltemplate AT chaowang mpcrnetmultiplepartialpointcloudsregistrationnetworkusingaglobaltemplate AT kechen mpcrnetmultiplepartialpointcloudsregistrationnetworkusingaglobaltemplate AT jianzhang mpcrnetmultiplepartialpointcloudsregistrationnetworkusingaglobaltemplate AT huiyang mpcrnetmultiplepartialpointcloudsregistrationnetworkusingaglobaltemplate
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MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

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