Labelled Indoor Point Cloud Dataset for BIM Related Applications

Nuno Abreu (), Rayssa Souza, Andry Pinto, Anibal Matos and Miguel Pires
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Nuno Abreu: INESC TEC, 4200-465 Porto, Portugal
Rayssa Souza: INESC TEC, 4200-465 Porto, Portugal
Andry Pinto: INESC TEC, 4200-465 Porto, Portugal
Anibal Matos: INESC TEC, 4200-465 Porto, Portugal
Miguel Pires: Grupo Casais, 4700-565 Braga, Portugal

Data, 2023, vol. 8, issue 6, 1-19

Abstract: BIM (building information modelling) has gained wider acceptance in the AEC (architecture, engineering, and construction) industry. Conversion from 3D point cloud data to vector BIM data remains a challenging and labour-intensive process, but particularly relevant during various stages of a project lifecycle. While the challenges associated with processing very large 3D point cloud datasets are widely known, there is a pressing need for intelligent geometric feature extraction and reconstruction algorithms for automated point cloud processing. Compared to outdoor scene reconstruction, indoor scenes are challenging since they usually contain high amounts of clutter. This dataset comprises the indoor point cloud obtained by scanning four different rooms (including a hallway): two office workspaces, a workshop, and a laboratory including a water tank. The scanned space is located at the Electrical and Computer Engineering department of the Faculty of Engineering of the University of Porto. The dataset is fully labelled, containing major structural elements like walls, floor, ceiling, windows, and doors, as well as furniture, movable objects, clutter, and scanning noise. The dataset also contains an as-built BIM that can be used as a reference, making it suitable for being used in Scan-to-BIM and Scan-vs-BIM applications. For demonstration purposes, a Scan-vs-BIM change detection application is described, detailing each of the main data processing steps.

Keywords: laser scanning; point cloud; indoor reconstruction; BIM; scan-to-BIM; scan-vs-BIM (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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