A review of rigid point cloud registration based on deep learning
With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registrati...
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| Published in | Frontiers in neurorobotics Vol. 17; p. 1281332 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Switzerland
Frontiers Research Foundation
04.01.2024
Frontiers Media S.A |
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| Abstract | With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration. |
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| AbstractList | With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration. |
| Author | Feng, Changzhou Zhao, Yikai Wang, Chaorong Ma, Yunpeng Chen, Lei |
| AuthorAffiliation | School of Information Engineering, Tianjin University of Commerce , Tianjin , China |
| AuthorAffiliation_xml | – name: School of Information Engineering, Tianjin University of Commerce , Tianjin , China |
| Author_xml | – sequence: 1 givenname: Lei surname: Chen fullname: Chen, Lei organization: School of Information Engineering, Tianjin University of Commerce, Tianjin, China – sequence: 2 givenname: Changzhou surname: Feng fullname: Feng, Changzhou organization: School of Information Engineering, Tianjin University of Commerce, Tianjin, China – sequence: 3 givenname: Yunpeng surname: Ma fullname: Ma, Yunpeng organization: School of Information Engineering, Tianjin University of Commerce, Tianjin, China – sequence: 4 givenname: Yikai surname: Zhao fullname: Zhao, Yikai organization: School of Information Engineering, Tianjin University of Commerce, Tianjin, China – sequence: 5 givenname: Chaorong surname: Wang fullname: Wang, Chaorong organization: School of Information Engineering, Tianjin University of Commerce, Tianjin, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38239758$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2024 Chen, Feng, Ma, Zhao and Wang. 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2024 Chen, Feng, Ma, Zhao and Wang. 2024 Chen, Feng, Ma, Zhao and Wang |
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| Keywords | deep learning partial overlap point cloud registration neural networks network acceleration |
| Language | English |
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| Snippet | With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration... |
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| StartPage | 1281332 |
| SubjectTerms | Data processing Deep learning Methods network acceleration neural networks Neuroscience Optimization techniques partial overlap point cloud registration Registration Reviews Software |
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| Title | A review of rigid point cloud registration based on deep learning |
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