A point cloud dataset and deep learning method for semantic segmentation of underground garages

Reconstructing as-built building information modeling from point clouds in underground garages holds great potential for enhancing facility management. However, this task is particularly challenging due to the complex and diverse architectural and mechanical, electrical, and plumbing (MEP) components present in such environments. Moreover, there is no publicly available dataset and no dedicated semantic segmentation algorithm for underground garages environments. To address this gap, this paper first builds an expert-labeled underground garage dataset, which includes 10 categories of components. Subsequently, this paper introduces GarageNet, a deep learning (DL) network designed for semantic segmentation of point clouds for scenes including both building and MEP components. GarageNet integrates a residual squeeze-and-excitation module to enhance layer and channel connection and a multi-head attention feature aggregation module to capture local geometric structures. The results indicate that the GarageNet achieved a high overall accuracy of 95.09% and a mean intersection over union of 87.15%, surpassing other DL algorithms such as PointNext. Ablation studies and cross-validation further demonstrate the model's reliability and generalization capability.