GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem....

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Autores principales:	Jianjun Zou, Zhenxin Zhang, Dong Chen, Qinghua Li, Lan Sun, Ruofei Zhong, Liqiang Zhang, Jinghan Sha
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	TLS point cloud registration urban scene GACM graph attention capsule network Science Q
Acceso en línea:	https://doaj.org/article/3bceed96fb7c41729228d5c11d7d95af
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id	oai:doaj.org-article:3bceed96fb7c41729228d5c11d7d95af
record_format	dspace
spelling	oai:doaj.org-article:3bceed96fb7c41729228d5c11d7d95af2021-11-25T18:53:40ZGACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene10.3390/rs132244972072-4292https://doaj.org/article/3bceed96fb7c41729228d5c11d7d95af2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4497https://doaj.org/toc/2072-4292Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.Jianjun ZouZhenxin ZhangDong ChenQinghua LiLan SunRuofei ZhongLiqiang ZhangJinghan ShaMDPI AGarticleTLS point cloud registrationurban sceneGACMgraph attentioncapsule networkScienceQENRemote Sensing, Vol 13, Iss 4497, p 4497 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	TLS point cloud registration urban scene GACM graph attention capsule network Science Q
spellingShingle	TLS point cloud registration urban scene GACM graph attention capsule network Science Q Jianjun Zou Zhenxin Zhang Dong Chen Qinghua Li Lan Sun Ruofei Zhong Liqiang Zhang Jinghan Sha GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
description	Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.
format	article
author	Jianjun Zou Zhenxin Zhang Dong Chen Qinghua Li Lan Sun Ruofei Zhong Liqiang Zhang Jinghan Sha
author_facet	Jianjun Zou Zhenxin Zhang Dong Chen Qinghua Li Lan Sun Ruofei Zhong Liqiang Zhang Jinghan Sha
author_sort	Jianjun Zou
title	GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
title_short	GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
title_full	GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
title_fullStr	GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
title_full_unstemmed	GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene
title_sort	gacm: a graph attention capsule model for the registration of tls point clouds in the urban scene
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3bceed96fb7c41729228d5c11d7d95af
work_keys_str_mv	AT jianjunzou gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT zhenxinzhang gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT dongchen gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT qinghuali gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT lansun gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT ruofeizhong gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT liqiangzhang gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene AT jinghansha gacmagraphattentioncapsulemodelfortheregistrationoftlspointcloudsintheurbanscene
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GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

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