Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM. (October 2018)

• Record Type: Journal Article
• Authors: Jung, Jaehoon; Stachniss, Cyrill; Ju, Sungha; Heo, Joon
• Is Part Of: Advanced engineering informatics. Volume 38(2018)
• Page Start: 811
• Page End: 825
• Publication Date: 2018-10
• Subjects: Point cloud -- As-built BIM -- Volumetric model; BIM Building Information Model -- SVM Support Vector Machine -- IFC Industry Foundation Classes -- LOD Level Of Detail -- EADE Euclidean Average Distance Error -- RMSE Root Mean Square Error -- SAS Spherical Accuracy Standard; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.10.007
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089 m and 0.074 m, respectively.