BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach

The demand for the models of BIM has increased due to the numerous applications in emergency response and location-based services. Creating a semantically rich 3D interior model has traditionally been a very manual process. Although the latest LiDAR scanning and photogrammetry techniques efficiently capture the existing building, the automatic reconstruction of complete volumetric and functional interior models is still an intensive and challenging research topic. This paper presents a novel parametric modeling method for reconstructing semantic volumetric building interiors from the unstructured point cloud of a building. Unlike existing partitioning-based methods, our proposed method overcomes the limitations by providing a flexible framework for combining 3D Deep Learning and an improved morphological approach for inverse BIM modeling. By employing a flexible and robust three-step mechanism, the proposed method first classifies the point cloud into thirteen types using a 3D Deep Learning method, then extracts the surfaces and generates the initial feature space. In regularizing the space and modeling BIM, the space boundaries are optimized by formulating the subordination between the grid cells generated by walls and the segmented boundary shapes generated by the gridded floor plan through energy minimization. Then, a grammar-enhanced point-line polygon and parametric description are designed to generate the final BIM model. By using a robust space partitioning and optimization method, three main goals are achieved: recovery of geometric and semantic information, robustness in different data sources (especially in non-Manhattan scenes), parametric BIM modeling in our proposed method. The paper demonstrates the potential of our algorithms for both RGB-D and LiDAR point clouds acquired from scenes in Manhattan and outside Manhattan. Experimental results show that this method is capable of automatically generating a geometrically and semantically consistent BIM model that is competitive with the existing method in terms of geometric accuracy and model completeness.