Repository logo

Matching design-intent planar, curved, and linear structural instances in point clouds

Accepted version



Change log


Brilakis, Ioannis 


The lack of timely progress monitoring and quality control contributes to cost-escalation, lowering of productivity, and broadly poor project performance. This paper addressed the challenge of high-precision structural instance segmentation from point clouds by leveraging as-designed IFC models in Scan-vs-BIM contexts. We proposed an automatic method to segment the entire points corresponding to the as-designed instance. The workflow contains: 1) Instance descriptor generation; 2) PROSAC-based shape detection; 3) DBSCAN-based cluster optimization. The method matches design-intent planar, curved, and linear structural instances in complex scenarios including: 1) the as-built point cloud is noisy with high occlusions and clutter; 2) deviations between as-built instances and as-designed models in terms of position, orientation, and scale; 3) both Manhattan-World and non-Manhattan-World instances. The experimental results from five diverse real-world datasets showed excellent performance with mPrecision 0.962, mRecall 0.934, and mIoU 0.914. Benchmarking against state-of-the-art methods showed that the proposed method outperforms all existing ones.



40 Engineering, Biotechnology, Generic health relevance

Journal Title

Automation in Construction

Conference Name

Journal ISSN


Volume Title


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