基于航空点云的电力网络学习式三维重建
Learning-Based 3D Reconstruction of Power Networks from Aerial Point Clouds
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中文总结 AI 辅助
本文提出一种端到端框架，利用改进的KPConv分割和两阶段拓扑推断，从航空LiDAR点云中重建电力网络并提取跨距级物理元数据，在真实数据集上实现分米级高度精度和约9%的召回率提升。
中文摘要 AI 辅助
本文提出了一种端到端框架，用于从大规模航空LiDAR数据中重建架空电力公用事业网络拓扑，并提取跨距级别的物理元数据。该流程首先使用改进的基于KPConv的模型对输入点云进行语义分割，其中数据采样和损失函数经过调整以强调电杆和导线（电线）类别。网络拓扑推断随后分两个阶段进行：（i）通过聚类电杆类点并使用几何标准（包括通过PCA估计的高度和垂直度）验证候选对象来获得电杆实例；（ii）使用启发式方法和轻量级基于ResNet的分类器，在电杆和导线点分布的二维俯视图投影上评估候选电杆对，以确定是否存在物理导线跨距。通过显式分类候选跨距，该方法减轻了在密集或杂乱场景以及部分导线观测下启发式连接规则的常见失败模式。对于每条验证的导线，计算与公用事业基础设施几何相关的属性，包括端点导线高度、地面高程、与垂度相关的最低点特征、导线布置和导线宽度。在多个真实航空LiDAR数据集上的评估表明，端点高度精度达到分米级，与启发式最近邻基线相比，拓扑重建的召回率相对提高约9%，在复杂布局中提升更大。
英文摘要
This paper presents an end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata from large-scale aerial LiDAR. The pipeline begins with semantic segmentation of the input point cloud using an improved KPConv-based model, in which data sampling and loss functions are adapted to emphasize pole and conductor (wire) classes. Network topology inference then proceeds in two stages: (i) pole instances are obtained by clustering pole-class points and validating candidates using geometric criteria, including height and verticality estimated via PCA, and (ii) candidate pole pairs are evaluated using a heuristic method and a lightweight ResNet-based classifier on 2D top-view projections of pole and wire point distributions to determine whether a physical conductor span exists. By explicitly classifying candidate spans, the approach mitigates common failure modes of heuristic connectivity rules in dense or cluttered scenes and under partial wire observation. For each validated wire, attributes regarding utility infrastructure geometry are computed, including endpoint conductor heights, ground elevation, sag-related lowest-point features, conductor arrangement, and wire width. Evaluation on multiple real-world aerial LiDAR datasets demonstrates decimeter-level endpoint height accuracy and approximately 9% relative improvement in recall for topology reconstruction compared to heuristic nearest-neighbor baselines, with larger gains in complex layouts.
发表机构
- AiDash（AiDash公司）
机构由 AI 辅助整理，请以论文原文为准。
