3D building model generation from MLS point cloud and 3D mesh using multi-source data fusion

The high-precision generation of 3D building models is a controversial research topic in the field of smart cities. However, due to the limitations of single-source data, existing methods cannot simultaneously balance the local accuracy, overall integrity, and data scale of the building model. In this paper, we propose a novel 3D building model generation method based on multi-source 3D data fusion of 3D point cloud data and 3D mesh data with deep learning method. First, A Multi-Source 3D data Quality Evaluation Network (MS3DQE-Net) is proposed for evaluating the quality of 3D meshes and 3D point clouds. Then, the evaluation results are utilized to guide 3D building model generation. The MS3DQE-Net uses 3D meshes and 3D point clouds as inputs and fuses the learned features to obtain a more complete shape description. To train MS3DQE-Net, a multi-source 3D dataset is constructed, which collected from a real scene based on mobile laser scanning (MLS) 3D point clouds and 3D mesh data, including pairs of matching 3D meshes and 3D point clouds of the 3D building model. Specifically, to our knowledge, we are the first researchers to propose such multi-source 3D dataset. The experimental results show that MS3DQE-Net achieves a state-of-the-art performance in multi-source 3D data quality evaluation. We demonstrate the large-scale and high-precision, 3D building model generation approach on a campus.