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PriSeg: IFC-supported Primitive Instance Geometry Segmentation with Unsupervised Clustering

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Peer-reviewed

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Authors

Brilakis, Ioannis 

Abstract

One of the societal problems for current building construction projects is the lack of timely progress monitoring and quality control, causing over-budget costs, inefficient productivity, and poor performance. This paper addresses the challenge of high-accuracy primitive instance segmentation from point clouds with the support of IFC model as a core stage to facilitate maintaining a geometric digital twin during the construction stage. Keeping the geometry of a building digital twin dynamic and up-to-date will help monitor and control the progress and quality timely and efficiently. We propose a novel automatic method named Priseg to detect and segment the entire points corresponding to the as-designed instance by developing an IFC-based instance descriptor and unsupervised clustering algorithm. The proposed solution is robust in real complex environments, such as point clouds are noisy with high occlusions and clutter, the as-built status deviates from the as-designed model in terms of position, orientation, and scale.

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Keywords

BIM, Digital Twin, Geometry, IFC, Instance Segmentation, Scan-vs-BIM

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European Conference on Computer Vision (ECCV) 2022 Workshop

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Sponsorship
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (860555)
European Commission’s Horizon 2020 for CBIM (Cloud-based Building Information Modelling) European Training Network under agreement No.860555
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