Improved building MEP systems semantic segmentation in point clouds using a novel multi-class dataset and local–global vector transformer network

Point cloud semantic segmentation for mechanical, electrical, and plumbing (MEP) systems is crucial for establishing MEP systems digital twins. Deep learning has shown promise in automatically deriving point-wise semantic labels from point clouds. However, the utilization of 3D deep learning methods for directly segmenting complex, occluded, and densely distributed multi-class MEP systems in common building point clouds remains unexplored due to the lack of specialized public datasets. To address this, our study introduces a public, richly annotated, and multi-class point cloud dataset for MEP systems in common buildings, consisting of 92.10 million points across four buildings, 56 areas, and nine component categories. Subsequently, we propose Trans2Net, a novel and customized 3D deep learning method for the automatic and precise segmentation of MEP components, featuring local vector transformer module, global-aid vector transformer module, and learnable feature fusion operator that enhance the model's sensitivity to local features, capture global context with sparse points, and dynamically fuse weighted local and global features. Experimental results demonstrate that Trans2Net significantly outperforms seven other state-of-the-art methods on the MEP dataset, achieving an overall accuracy of 97.39%, a mean accuracy of 95.45%, and a mean intersection over union (mIoU) of 90.76%. Notably, it surpasses the second-ranked method, PointMeta, by a margin of 9.91% in mIoU. This study provides a novel paradigm for automatically understanding MEP systems in point clouds and offers robust support for digital twin management of MEP systems, contributing to the prolonged lifespans of MEP systems.