Deep Learning–Based Scan-to-BIM Automation and Object Scope Expansion Using a Low-Cost 3D Scan Data
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
To bridge the gap in as-built Building Information Model (BIM) creation between the architectural, engineering, and construction (AEC) community and the computer vision community, this paper presents an automated Scan-to-BIM framework for modeling both structural and nonstructural building components using a low-cost scanning data. The state-of-the-art instance-level semantic segmentation algorithm, SoftGroup, is adopted to classify individual building components. Detected wall segments are projected onto a two-dimensional (2D) XY grid, and an interest point detection algorithm, SuperPoint, is used to extract wall corner points. Subsequently, a series of refinement steps is proposed to generate the wall boundary. With optimized parameters, an intersection-over-union of 82.56% was achieved when tested on the benchmark Stanford Three-Dimensional (3D) Indoor Scene Data Set. Our results demonstrated the usability of the proposed wall boundary extraction to the incomplete and complex indoor scan data compared to an existing as-built modeling method. Instance-level segments and the refined wall boundary were combined to generate as-built BIM via parametric modeling.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Journal of Computing in Civil Engineering
Volume 38 • Issue 6 • November 2024
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© 2024 American Society of Civil Engineers.
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Received: Sep 29, 2023
Accepted: Apr 26, 2024
Published online: Sep 2, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 2, 2025
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