3D reconstruction of a large-scale electrical substation scene (ESS) is fundamental to navigation, information inquiry, and supervisory control of 3D scenes. However, automatic reconstruction of ESS from a raw LiDAR point cloud is challenging due to its incompleteness, noise and anisotropy in density. We propose an automatic and efficient approach to reconstruct ESSs, by mapping raw LiDAR data to our well-established electrical device database (EDD). We derive a flexible and hierarchical representation of the ESS automatically by exploring the internal topology of the corresponding LiDAR data, followed by extracting various devices from the ESS. For each device, a quality mesh model is retrieved in the EDD, based on the proposed object descriptor that can balance descriptiveness, robustness and efficiency. With the high-level representation of the ESS, we map all retrieved models into raw data to achieve a high-fidelity scene reconstruction. Extensive experiments on large and complex ESSs modeling demonstrate the efficiency and accuracy of the proposed method.

大型变电站场景（ESS）的3D重建对于3D场景的导航，信息查询和监督控制至关重要。然而，由于其不完整，噪声和密度各向异性，从原始LiDAR点云自动重建ESS具有挑战性。通过将原始LiDAR数据映射到我们建立良好的电气设备数据库（EDD），我们提出了一种自动高效的方法来重建ESS。通过探索相应LiDAR数据的内部拓扑，然后从ESS中提取各种设备，我们可以自动得出ESS的灵活且分层的表示形式。对于每个设备，基于建议的对象描述符（可在描述性，鲁棒性和效率之间取得平衡）在EDD中检索高质量的网格模型。利用ESS的高级表示，我们将所有检索到的模型映射到原始数据中，以实现高保真场景重建。在大型和复杂的ESS建模上的大量实验证明了该方法的有效性和准确性。