Conference Agenda

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Session Overview
Overview of EC3, Summer School Highlights and Keynote: 2nd Ranked Paper
Tuesday, 27/July/2021:
10:00am - 11:00am

Session Chair: James Thomas O'Donnell
Session Chair: Symeon Christodoulou

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Assessing IFC classes with means of geometric deep learning on different graph encodings*

Fiona C. Collins1, Alexander Braun1, Martin Ringsquandl2, Daniel M. Hall3, André Borrmann1

1Technical University of Munich, Germany; 2Siemens AG, Corporate Technology, Munich, Germany; 3ETH Zürich, Switzerland

Machine-readable Building Information Models (BIM) are of great benefit for the building operation phase. Losses through data exchange or issues in software interoperability can significantly impede their availability. Incorrect and imprecise semantics in the exchange format IFC are frequent and complicate knowledge extraction. To support an automated IFC object correction, we use a Geometric Deep Learning (GDL) approach to perform classification based solely on the 3D shape. A Graph Convolutional Network (GCN) uses the native triangle-mesh and automatically creates meaningful local features for subsequent classification. The method reaches an accuracy of up to 85\% on our self-assembled, partially industry dataset.

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