TY - GEN
T1 - Artificial Neural Network for Semantic Segmentation of Built Environments for Automated Scan2BIM
AU - Perez-Perez, Yeritza
AU - Golparvar-Fard, Mani
AU - El-Rayes, Khaled
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - 3D modeling of the built environment has become common practice in the AEC/FM industry. Practitioners take advantage of the geometric and semantic information embedded in the 3D model to perform engineering analysis. Despite the benefits provide by the 3D model, the process is time-consuming, labor-intensive, and error-prone. In this paper, we propose a new neural network-based method for 3D point cloud semantic segmentation of building scenes using a hierarchical approach: first, we reason on the local and global contents of raw point cloud data to extract geometrical features. Second, the features are used as input to an artificial neural network that performs semantic segmentation on the points. These points are classified into: beam, ceiling, clutter, column, door, floor, pipe, wall, and window. We evaluated our approach on a dataset of several buildings and we obtained an accuracy of 73%. Our experiments produce robust results readily useful for practical Scan2BIM applications.
AB - 3D modeling of the built environment has become common practice in the AEC/FM industry. Practitioners take advantage of the geometric and semantic information embedded in the 3D model to perform engineering analysis. Despite the benefits provide by the 3D model, the process is time-consuming, labor-intensive, and error-prone. In this paper, we propose a new neural network-based method for 3D point cloud semantic segmentation of building scenes using a hierarchical approach: first, we reason on the local and global contents of raw point cloud data to extract geometrical features. Second, the features are used as input to an artificial neural network that performs semantic segmentation on the points. These points are classified into: beam, ceiling, clutter, column, door, floor, pipe, wall, and window. We evaluated our approach on a dataset of several buildings and we obtained an accuracy of 73%. Our experiments produce robust results readily useful for practical Scan2BIM applications.
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M3 - Conference contribution
AN - SCOPUS:85092227651
T3 - Computing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 97
EP - 104
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019
Y2 - 17 June 2019 through 19 June 2019
ER -