Deep Learning–Based Automation of Scan-to-BIM with Modeling Objects from Occluded Point Clouds
Publication: Journal of Management in Engineering
Volume 38, Issue 4
Abstract
As-built building information modeling (BIM) currently is regarded as a tool with the potential to manage buildings efficiently in the operation and maintenance phases. However, as-built BIM modeling is a labor-intensive process that requires considerable cost and time in modeling existing buildings. Although active research on scan-to-BIM automation has addressed this issue, previous studies modeled only major objects such as walls, floors, and ceilings, consequently requiring modeling other objects in indoor spaces. In addition, there was a limitation in modeling objects located in the occluded areas of scanned point clouds. Therefore, this study extracted various indoor objects from a point cloud based on deep-learning, and compensated for incomplete object information from occluded point clouds for automating the process of scan-to-BIM. The number of object classes extracted from the semantic segmentation of a deep learning network was increased to 13, and spatial relationships between objects were defined to improve the accuracy of bounding boxes extracted from point clouds. Furthermore, a parametric algorithm was developed to match the bounding boxes and objects in a BIM library to generate BIM models automatically. In a case study involving an office room, the accuracy of the bounding boxes of some object classes improved by as much as 53.33%. The study verified the feasibility of the proposed method of scan-to-BIM automation for the three-dimensional (3D) reality capture of existing buildings.
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Data Availability Statement
Some of all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by Ministry of Science and ICT (NRF-2020R1A4A2002855), and the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22AATD-C163269-02).
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Received: Nov 20, 2021
Accepted: Feb 18, 2022
Published online: Mar 28, 2022
Published in print: Jul 1, 2022
Discussion open until: Aug 28, 2022
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