Reconstructing Occluded Power Lines from LiDAR Data: Current State, Methods, and Future Research

Mobile LiDAR technology is increasingly employed to generate detailed 3D point clouds of electrical transmission infrastructure, where power lines appear as slender, suspended elements. However, these lines are frequently fragmented or absent in the data due to occlusions caused by vegetation, built structures, or unfavorable scanning geometries, posing significant challenges for automated reconstruction in complex environments. This survey offers a comprehensive review of existing methods designed to reconstruct occluded or incomplete power lines from LiDAR data, classifying them into four main categories: geometric fitting techniques (e.g., parabolic and catenary models), machine learning (ML) and deep learning (DL) approaches, graph-based strategies, and hybrid models that integrate physical priors with data-driven inference. Each method is examined with respect to its robustness to occlusion, computation efficiency, data requirements, and reconstruction accuracy. The paper also discusses key evaluation metrics. It concludes by outlining open challenges and future research directions, including generative modeling under severe occlusion, multimodal data fusion, and real-Time reconstruction for operational deployment in power grid monitoring systems.