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.
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