Potential of Deep Learning Models for Point Cloud-Based Infrastructure Management

The increasing recognition within the infrastructure sector of the transformative potential of 3D point cloud data for civil structure management has prompted a growing interest. However, the inherent complexity of these data poses significant challenges. With the expanding accessibility to point cloud data and the rising demand for robust infrastructure management, the strategic application of deep learning becomes opportune. Deep learning models exhibit promise in various tasks, including object visualization, anomaly detection, element classification, and component segmentation. Addressing a notable research gap between point cloud technology and its allied fields, this review provides a comprehensive overview of deep learning models specifically tailored for Civil Infrastructure Management. Commencing with an exploration of the core principles underlying foundational models such as CNN, GNN, PointNet, and ResNet, the discussion progresses to advanced architectures, including DGCNN and ResPointNet++. Through a comparative analysis, this review delineates pathways for advancing deep learning models, with a particular emphasis on integrating domain knowledge and streamlining architectural designs. The findings contribute valuable insights aimed at developing more effective approaches for leveraging deep learning in point cloud-based infrastructure management, aligning with the dynamic demands of the industry. This paper centers on the strategic utilization of deep learning to address complex infrastructure challenges, providing insights that are indispensable for staying aligned with the evolving landscape of the industry.