Shape Classification of Building Information Models using Neural Networks

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dc.contributor.authorEvangelou, Iordanisen_US
dc.contributor.authorVitsas, Nicken_US
dc.contributor.authorPapaioannou, Georgiosen_US
dc.contributor.authorGeorgioudakis, Manolisen_US
dc.contributor.authorChatzisymeon, Apostolosen_US
dc.contributor.editorBiasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.en_US
dc.date.accessioned2021-09-01T08:25:35Z
dc.date.available2021-09-01T08:25:35Z
dc.date.issued2021
dc.description.abstractThe Building Information Modelling (BIM) procedure introduces specifications and data exchange formats widely used by the construction industry to describe functional and geometric elements of building structures in the design, planning, cost estimation and construction phases of large civil engineering projects. In this paper we explain how to apply a modern, low-parameter, neural-network-based classification solution to the automatic geometric BIM element labeling, which is becoming an increasingly important task in software solutions for the construction industry. The network is designed so that it extracts features regarding general shape, scale and aspect ratio of each BIM element and be extremely fast during training and prediction. We evaluate our network architecture on a real BIM dataset and showcase accuracy that is difficult to achieve with a generic 3D shape classification network.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20211306
dc.identifier.isbn978-3-03868-137-3
dc.identifier.issn1997-0471
dc.identifier.pages1-4
dc.identifier.urihttps://doi.org/10.2312/3dor.20211306
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20211306
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectNeural networks
dc.subjectShape analysis
dc.titleShape Classification of Building Information Models using Neural Networksen_US
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