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.

中文翻译：

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PylonModeler：一种用于 LiDAR 点云输电塔架的混合驱动 3D 重建方法


由于电网是现代社会不可或缺的基础，因此创建电网的数字孪生非常重要。塔架是输电走廊的组成部分，其精确的 3D 重建对于电网的安全运行至关重要。然而，由于数据质量以及塔架结构的多样性和复杂性，从 LiDAR 点云重建 3D 塔架带来了许多挑战。为了应对这些挑战，我们推出了 PylonModeler：一种使用机载 LiDAR 点云进行 3D 塔架重建的混合驱动方法，从而实现准确、稳健和高效的实时塔架重建。采用不同的策略来实现各种结构的独立重建和组装。我们提出了 Pylon Former，这是一种用于实时塔架识别和分解的轻量级 transformer 网络。随后，我们应用数据驱动的方法进行塔体重建。考虑到结构特征，使用拟合和聚类算法来重建外部和内部结构。塔架头采用混合方法重建。预构建的塔头参数模型库通过一系列参数定义不同的塔架。采用相干点漂移 （CPD） 算法建立塔架头部结构之间的拓扑关系并设置初始模型参数，并通过优化对其进行改进，以实现准确的塔架头部重建。最后，将塔体和头部模型组合在一起，完成重建。我们收集了一个机载 LiDAR 数据集，其中包括八种类型的 3398 个塔架数据。该数据集由各种电压等级的输电线路组成，例如 110 kV、220 kV 和 500 kV。 PylonModeler 在此数据集上进行了验证。塔架的平均重建时间为 1.10 s，平均重建精度为 0.216 m。此外，我们还评估了 PylonModeler 在卢森堡公共机载 LiDAR 数据上的性能。与以前的最先进方法相比，重建精度提高了约 26.28 %。PylonModeler 具有卓越的性能，比当前的模型驱动方法快数十倍，从而支持实时塔架重建。