High quality 3D reconstruction of indoor environments using RGB-D sensors

Wang, J; Huang, S; Zhao, L; Ge, J; He, S; Zhang, C; Wang, X

High quality 3D reconstruction of indoor environments using RGB-D sensors

Wang, J Huang, S

Zhao, L

Ge, J

He, S

Zhang, C

Wang, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1739 - 1744
Issue Date:: 2018-02-05

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Field	Value	Language
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183	en_US
dc.contributor.author	Ge, J https://orcid.org/0000-0002-2252-7829	en_US
dc.contributor.author	He, S https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Wang, X	en_US
dc.date.issued	2018-02-05	en_US
dc.identifier.citation	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 1739 - 1744	en_US
dc.identifier.isbn	9781538621035	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116471
dc.description.abstract	© 2017 IEEE. High-quality 3D reconstruction of large-scale indoor scene is the key to combine Simultaneous Localization And Mapping (SLAM) with other applications, such as building inspection and construction monitoring. However, the requirement of global consistency brings challenges to both localization and mapping. In particular, significant localization and mapping error can happen when standard SLAM techniques are used when dealing with the area of featureless walls and roofs. This paper proposed a novel framework aiming to reconstruct a high-quality, globally consistent 3D model for indoor environments using only a RGB-D sensor. We first introduce the sparse and dense feature constraints in the local bundle adjustment. Then, the planar constraints are incorporated in the global bundle adjustment. We fuse the point clouds in a truncated signed distance function volume, from which the high quality mesh can be extracted. Our framework leads to a comprehensive 3D scanning solution for indoor scene, enabling high-quality results and potential applications in building information system. The video of 3D models reconstructed by the method proposed in this paper is available at https://youtu.be/DWMP4YfeNeY.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017	en_US
dc.relation.isbasedon	10.1109/ICIEA.2017.8283120	en_US
dc.title	High quality 3D reconstruction of indoor environments using RGB-D sensors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-February	en_US
utslib.for	1202 Building	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-February	en_US

Abstract:

© 2017 IEEE. High-quality 3D reconstruction of large-scale indoor scene is the key to combine Simultaneous Localization And Mapping (SLAM) with other applications, such as building inspection and construction monitoring. However, the requirement of global consistency brings challenges to both localization and mapping. In particular, significant localization and mapping error can happen when standard SLAM techniques are used when dealing with the area of featureless walls and roofs. This paper proposed a novel framework aiming to reconstruct a high-quality, globally consistent 3D model for indoor environments using only a RGB-D sensor. We first introduce the sparse and dense feature constraints in the local bundle adjustment. Then, the planar constraints are incorporated in the global bundle adjustment. We fuse the point clouds in a truncated signed distance function volume, from which the high quality mesh can be extracted. Our framework leads to a comprehensive 3D scanning solution for indoor scene, enabling high-quality results and potential applications in building information system. The video of 3D models reconstructed by the method proposed in this paper is available at https://youtu.be/DWMP4YfeNeY.

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http://hdl.handle.net/10453/116471