Verification of 3D Electrical Equipment Model Based on Cross-source Point Cloud Registration Using Deep Neural Netwo

Abstract

With the popularization of digital twin techniques in power substations, assessment and verification of electrical equipment 3D models in digital twins according to as-built LiDAR point clouds become essential for the quality assurance of the designed substation models. However, computing the shape and texture differences between a 3D model and its corresponding point cloud is challenging due to the difficulty in aligning cross-source equipment point clouds with local geometric shape variations. In this paper, we propose a 3D model verification method based on overlap-aware cross-source point cloud registration. The key of the method is an overlap attention-based point cloud registration network with grouped KPConv, attention mechanism, and overlap-weighted circle loss. It improves the registration accuracy against local geometric shape variations between 3D models and LiDAR point clouds. In addition, due to the lack of real-world point cloud samples of electrical equipment, a novel point cloud augmentation method is employed for generating synthetic point clouds for improving the sim-to-real generalization capability of the network. Based on the pose alignment of the 3D model and the corresponding point cloud, a facet-level computing method is proposed for model differentiation and colorization. Experimental results using real-world point clouds of power substation equipment validate the performance of the proposed method.