Mixed Reality-Based Dataset Generation for Learning-Based Scan-to-BIM

Bhadaniya, P; Reja, VK; Varghese, K

Mixed Reality-Based Dataset Generation for Learning-Based Scan-to-BIM

Bhadaniya, P Reja, VK Varghese, K

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Pattern Recognition. ICPR International Workshops and Challenges, 2021, 12667 LNCS, pp. 389-403
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Bhadaniya, P
dc.contributor.author	Reja, VK
dc.contributor.author	Varghese, K
dc.date	2021-01-10
dc.date.accessioned	2022-08-31T13:40:01Z
dc.date.available	2022-08-31T13:40:01Z
dc.date.issued	2021-01-01
dc.identifier.citation	Pattern Recognition. ICPR International Workshops and Challenges, 2021, 12667 LNCS, pp. 389-403
dc.identifier.isbn	9783030687861
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/161142
dc.description.abstract	Generating as-is 3D Models is constantly explored for various construction management applications. The industry has been dependent on either manual or semi-automated workflows for the Scan-to-BIM process, which is laborious as well as time taking. Recently machine learning has opened avenues to recognize geometrical elements from point clouds but has not been much used because of the insufficient labeled dataset. This study aims to set up a semi-automated workflow to create labeled data sets which can be used to train ML algorithms for element identification purpose. The study proposes an interactive user interface using a gaming engine within a mixed reality environment. A workflow for fusing as-is spatial information with the AR/VR based information is presented in Unity 3D. A user-friendly UI is then developed and integrated with the VR environment to help the user to choose the category of the component by visualization. This results in the generation of an accurate as-is 3D Model, which does not require much computation or time. The intention is to propose a smooth workflow to generate datasets for learning-based methodologies in a streamlined Scan-to-BIM Process. However, this process requires user domain knowledge and input. The dataset can be continuously increased and improved to get automated results later.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Pattern Recognition. ICPR International Workshops and Challenges
dc.relation.ispartof	International Conference on Pattern Recognition Workshops
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-68787-8_29
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Mixed Reality-Based Dataset Generation for Learning-Based Scan-to-BIM
dc.type	Conference Proceeding
utslib.citation.volume	12667 LNCS
utslib.location.activity	Virtual Event,
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-31T13:39:56Z
pubs.finish-date	2021-01-15
pubs.place-of-publication	Swizterland
pubs.publication-status	Published
pubs.start-date	2021-01-10
pubs.volume	12667 LNCS
dc.location	Swizterland

Abstract:

Generating as-is 3D Models is constantly explored for various construction management applications. The industry has been dependent on either manual or semi-automated workflows for the Scan-to-BIM process, which is laborious as well as time taking. Recently machine learning has opened avenues to recognize geometrical elements from point clouds but has not been much used because of the insufficient labeled dataset. This study aims to set up a semi-automated workflow to create labeled data sets which can be used to train ML algorithms for element identification purpose. The study proposes an interactive user interface using a gaming engine within a mixed reality environment. A workflow for fusing as-is spatial information with the AR/VR based information is presented in Unity 3D. A user-friendly UI is then developed and integrated with the VR environment to help the user to choose the category of the component by visualization. This results in the generation of an accurate as-is 3D Model, which does not require much computation or time. The intention is to propose a smooth workflow to generate datasets for learning-based methodologies in a streamlined Scan-to-BIM Process. However, this process requires user domain knowledge and input. The dataset can be continuously increased and improved to get automated results later.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/161142