Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud

Abstract

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.