Integrating Inverse Photogrammetry and a Deep Learning–Based Point Cloud Segmentation Approach for Automated Generation of BIM Models
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 9
Abstract
Automatically converting three-dimensional (3D) point clouds into building information modeling (BIM) has been an active research area over the past few years. However, existing solutions in the literature have been suffering the limitations of covering all different design scenarios (prior knowledge-based approach) or collecting sufficient point clouds as training data sets (3D deep learning–based approach). To tackle this issue, we propose a fused system to automatically develop as-built BIMs from photogrammetric point clouds. A series of images is captured to generate a high-quality point cloud, which is then preprocessed by removing noise and downsizing points. Meanwhile, a two-dimensional (2D) deep-learning method, DeepLab, is utilized to semantically segment elements (e.g., walls, slabs, and columns) from the collected images. Subsequently, an inverse photogrammetric pipeline is employed to recognize element categories in the point cloud by projecting the isolated 3D planes into 2D images and assigning the identified elements to the 3D planes. Finally, the industry foundation classes are devised to create as-built BIMs based on the segmented point clouds. In order to evaluate the performance of the proposed system, we selected six cases with various elements as the testbed. The prospective results reveal that (1) our system can provide a highly automated solution to develop as-built BIMs; and (2) 39 out of 45 elements in six different cases are successfully recognized in point clouds.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request.
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Journal of Construction Engineering and Management
Volume 149 • Issue 9 • September 2023
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© 2023 American Society of Civil Engineers.
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Received: Aug 11, 2022
Accepted: Mar 31, 2023
Published online: Jun 22, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 22, 2023
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