Automated Extraction of Indoor Structural Information from 3D Point Clouds
Keywords:
structural elements, 3D reconstruction, PCA, indoor modelling, point cloud, wire frameworkAbstract
In numerous smart city applications, including building information modeling (BIM), spatial location applications, energy consumption prediction, and signal simulation, accurate 3D modeling of interior environments is crucial. Rapid and stable reconstruction of three-dimensional models from point clouds has attracted considerable interest, but the creation of accurate three-dimensional models in complex interior environments remains a formidable challenge. This study presents a novel method for autonomously recreating 3D models by combining linear structures with three-dimensional geometric surfaces. Using 3D point clouds, the proposed method recognizes indoor structural frameworks. It uses a combination of Principal Component Analysis (PCA) variables, such as curvature, anisotropy, and verticality, to accurately detect and extract building structures. To evaluate the efficacy of the method, a dataset of real-world 3D point cloud scans is employed, and the results demonstrate its capacity to recognize structural frameworks with low Chamfer and Hausdorff distances. Doors, windows, and pillars are accurately reconstructed, allowing for an Indoor Structural Information model to be generated. This model can considerably improve building information modeling, construction planning, and maintenance tasks by automating BIM modeling. This method has the potential to improve the accuracy and efficacy of 3D reconstruction in smart city applications, allowing for more accurate building information modeling and streamlining construction and maintenance processes.
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