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Conference Paper: Markerless Augmented Reality for Facility Management: Automated Spatial Registration based on Style Transfer Generative Network

TitleMarkerless Augmented Reality for Facility Management: Automated Spatial Registration based on Style Transfer Generative Network
Authors
KeywordsAugmented reality
Building information model (BIM)
Facility management
Generative adversarial network (GAN)
Markerless spatial registration
Issue Date2021
Citation
38th International Symposium on Automation and Robotics in Construction (ISARC 2021), Dubai, 2-4 November 2021. In Proceedings of the International Symposium on Automation and Robotics in Construction, 2021, p. 467-474 How to Cite?
AbstractOn-demand and real-time building information is of great value to support facility management. Such information can be easily retrieved from an up-to-date building information model (BIM), and then intuitively presented to facility managers or inspectors by augmented reality (AR). However, effective spatial registration into BIM so as to align the virtual and real content still remains an unresolved challenge. Leveraging recent development in the field of generative networks, this paper proposes a markerless registration approach that can automatically align BIM with the real view captured by a mobile device without any manual operation. A mobile AR application is develop based on the proposed registration approach. Our field experiments demonstrate the effectiveness of the proposed approach for automated BIM registration. The successful registration thus allows users to access the rich building information, especially invisible utility such as the mechanical, electrical and plumbing (MEP) system, in the real-life context for better facility management practice.
Persistent Identifierhttp://hdl.handle.net/10722/323607

 

DC FieldValueLanguage
dc.contributor.authorChen, J-
dc.contributor.authorLi, SHUAI-
dc.contributor.authorLu, WW-
dc.contributor.authorLiu, DONGHAI-
dc.contributor.authorHu, DA-
dc.contributor.authorTang, M-
dc.date.accessioned2023-01-08T07:09:23Z-
dc.date.available2023-01-08T07:09:23Z-
dc.date.issued2021-
dc.identifier.citation38th International Symposium on Automation and Robotics in Construction (ISARC 2021), Dubai, 2-4 November 2021. In Proceedings of the International Symposium on Automation and Robotics in Construction, 2021, p. 467-474-
dc.identifier.urihttp://hdl.handle.net/10722/323607-
dc.description.abstractOn-demand and real-time building information is of great value to support facility management. Such information can be easily retrieved from an up-to-date building information model (BIM), and then intuitively presented to facility managers or inspectors by augmented reality (AR). However, effective spatial registration into BIM so as to align the virtual and real content still remains an unresolved challenge. Leveraging recent development in the field of generative networks, this paper proposes a markerless registration approach that can automatically align BIM with the real view captured by a mobile device without any manual operation. A mobile AR application is develop based on the proposed registration approach. Our field experiments demonstrate the effectiveness of the proposed approach for automated BIM registration. The successful registration thus allows users to access the rich building information, especially invisible utility such as the mechanical, electrical and plumbing (MEP) system, in the real-life context for better facility management practice.-
dc.languageeng-
dc.relation.ispartofProceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)-
dc.subjectAugmented reality-
dc.subjectBuilding information model (BIM)-
dc.subjectFacility management-
dc.subjectGenerative adversarial network (GAN)-
dc.subjectMarkerless spatial registration-
dc.titleMarkerless Augmented Reality for Facility Management: Automated Spatial Registration based on Style Transfer Generative Network-
dc.typeConference_Paper-
dc.identifier.emailChen, J: chenjj10@hku.hk-
dc.identifier.emailLu, WW: wilsonlu@hku.hk-
dc.identifier.authorityChen, J=rp03048-
dc.identifier.authorityLu, WW=rp01362-
dc.identifier.doi10.22260/ISARC2021/0064-
dc.identifier.scopuseid_2-s2.0-85121266990-
dc.identifier.hkuros343260-
dc.identifier.spage467-
dc.identifier.epage474-
dc.identifier.eissn2413-5844-

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