PylonModeler: A hybrid-driven 3D reconstruction method for power transmission pylons from LiDAR point clouds

As the power grid is an indispensable foundation of modern society, creating a digital twin of the grid is of great importance. Pylons serve as components in the transmission corridor, and their precise 3D reconstruction is essential for the safe operation of power grids. However, 3D pylon reconstruction from LiDAR point clouds presents numerous challenges due to data quality and the diversity and complexity of pylon structures. To address these challenges, we introduce PylonModeler: a hybrid-driven method for 3D pylon reconstruction using airborne LiDAR point clouds, thereby enabling accurate, robust, and efficient real-time pylon reconstruction. Different strategies are employed to achieve independent reconstructions and assemblies for various structures. We propose Pylon Former, a lightweight transformer network for real-time pylon recognition and decomposition. Subsequently, we apply a data-driven approach for the pylon body reconstruction. Considering structural characteristics, fitting and clustering algorithms are used to reconstruct both external and internal structures. The pylon head is reconstructed using a hybrid approach. A pre-built pylon head parameter model library defines different pylons by a series of parameters. The coherent point drift (CPD) algorithm is adopted to establish the topological relationships between pylon head structures and set initial model parameters, which are refined through optimization for accurate pylon head reconstruction. Finally, the pylon body and head models are combined to complete the reconstruction. We collected an airborne LiDAR dataset, which includes a total of 3398 pylon data across eight types. The dataset consists of transmission lines of various voltage levels, such as 110 kV, 220 kV, and 500 kV. PylonModeler is validated on this dataset. The average reconstruction time of a pylon is 1.10 s, with an average reconstruction accuracy of 0.216 m. In addition, we evaluate the performance of PylonModeler on public airborne LiDAR data from Luxembourg. Compared to previous state-of-the-art methods, reconstruction accuracy improved by approximately 26.28 %. With superior performance, PylonModeler is tens of times faster than the current model-driven methods, enabling real-time pylon reconstruction.