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Conference Paper: An optimization-based semantic building model generation method with a pilot case of a demolished construction

TitleAn optimization-based semantic building model generation method with a pilot case of a demolished construction
Authors
Issue Date2018
PublisherSpringer.
Citation
Proceedings of the 21st International Conference on Advancement of Construction Management and Real Estate (CRIOCM 2016), Faculty of Architecture, The University of Hong Kong, Hong Kong, 14-17 December 2016, p. 231-241 How to Cite?
Abstract(Winner of the only Best Paper Award) Emerging technologies like massive point cloud from laser scanning and 3D photogrammetry enabled new ways of generating ‘as-built’ building information models (BIM) for existing buildings. It is valuable but also challenging to generate semantic models from point cloud and images in automated ways. In this paper, we present a novel method called Optimization-based Model Generation (OMG) for automated semantic BIM generation. OMG starts from a semantic BIM component dataset and a target measurement such as point cloud, photographs, or floor plans. A fitness function is defined to measure the matching level between an arbitrary BIM model and the target measurement without object recognition. Combinations of digital components are then extensively generated as building models regarding semantic constraints. The fittest model that matches the target measurement best is the result of OMG. The proposed method was demonstrated in reconstructing a 3D model of a demolished building. Advantages of OMG include high-level automation, low requirement on measurement, relationship discovery for components, reusable component libraries, and scalability to new environments.
DescriptionSession 4A: Building Information Modelling
Persistent Identifierhttp://hdl.handle.net/10722/239321
ISBN

 

DC FieldValueLanguage
dc.contributor.authorXue, F-
dc.contributor.authorChen, K-
dc.contributor.authorLiu, D-
dc.contributor.authorNiu, Y-
dc.contributor.authorLu, W-
dc.date.accessioned2017-03-15T02:23:18Z-
dc.date.available2017-03-15T02:23:18Z-
dc.date.issued2018-
dc.identifier.citationProceedings of the 21st International Conference on Advancement of Construction Management and Real Estate (CRIOCM 2016), Faculty of Architecture, The University of Hong Kong, Hong Kong, 14-17 December 2016, p. 231-241-
dc.identifier.isbn978-981-10-6189-9-
dc.identifier.urihttp://hdl.handle.net/10722/239321-
dc.descriptionSession 4A: Building Information Modelling-
dc.description.abstract(Winner of the only Best Paper Award) Emerging technologies like massive point cloud from laser scanning and 3D photogrammetry enabled new ways of generating ‘as-built’ building information models (BIM) for existing buildings. It is valuable but also challenging to generate semantic models from point cloud and images in automated ways. In this paper, we present a novel method called Optimization-based Model Generation (OMG) for automated semantic BIM generation. OMG starts from a semantic BIM component dataset and a target measurement such as point cloud, photographs, or floor plans. A fitness function is defined to measure the matching level between an arbitrary BIM model and the target measurement without object recognition. Combinations of digital components are then extensively generated as building models regarding semantic constraints. The fittest model that matches the target measurement best is the result of OMG. The proposed method was demonstrated in reconstructing a 3D model of a demolished building. Advantages of OMG include high-level automation, low requirement on measurement, relationship discovery for components, reusable component libraries, and scalability to new environments.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofInternational Conference on Advancement of Construction Management & Real Estate, CRIOCM 2016-
dc.titleAn optimization-based semantic building model generation method with a pilot case of a demolished construction-
dc.typeConference_Paper-
dc.identifier.emailXue, F: xuef@hku.hk-
dc.identifier.emailLu, W: wilsonlu@hku.hk-
dc.identifier.authorityXue, F=rp02189-
dc.identifier.authorityLu, W=rp01362-
dc.identifier.doi10.1007/978-981-10-6190-5_22-
dc.identifier.hkuros271646-
dc.identifier.spage231-
dc.identifier.epage241-
dc.publisher.placeSingapore-

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