Research on BIM Reconstruction Method Using Semantic Segmentation Point Cloud Data Based on PointNet

Abstract As the construction industry is shifting from the construction of new buildings to the maintenance and use of existing buildings in recent years, the demand for automated building information models (BIM) creation is increasing. This paper uses the deep learning network PointNet to perform...

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Bibliographic Details
Published inIOP conference series. Earth and environmental science Vol. 719; no. 2; p. 22042
Main Authors Xiong, Zhaoyang, Wang, Ting
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2021
Subjects
Algorithms
Building components
Building information modeling
Building management systems
Buildings
Construction industry
Datasets
Image segmentation
Machine learning
Mathematical models
Parameters
Semantic segmentation
Semantics
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Summary:Abstract As the construction industry is shifting from the construction of new buildings to the maintenance and use of existing buildings in recent years, the demand for automated building information models (BIM) creation is increasing. This paper uses the deep learning network PointNet to perform semantic segmentation on the public S3DIS point cloud data set, which means to assign the same type of point cloud building components in the data set to the same label, and the bounding box algorithm is been used to obtain the outer contour parameters of the segmented point cloud building components. Finally, the Dynamo, which is one of the Revit plug-in, is used to perform parametric modeling according to the obtained parameters, and generates the BIM corresponding to the point cloud data set. The experimental results show that the method proposed in this paper can complete the parametric creation of BIM with high completeness based on the efficient segmentation of point clouds.
ISSN:1755-1307
1755-1315
DOI:10.1088/1755-1315/719/2/022042