A 3D Reconstruction Method for Heterogeneous Data of Power Equipment Based on Improved Neural Radiation Fields

In order to facilitate the digital transformation of conventional power grid, constructing the digital twin of power equipment is crucial. The primary task of the digital transformation is to build the 3D model of power equipment. However, existing methods cannot achieve digital twin modeling with high fidelity and efficiency. Therefore, a 3D reconstruction method for heterogenous data based on improved Neural Radiation Fields (NeRF) is proposed. By utilizing the infrared-visible image sequence, the spatial domain consistency of the heterogenous data is preserved by the image registration algorithm improved based on restricted matching region strategy (RMRS). Then, in order to focus on the characteristics of the equipment, a deep image matting network (DIM) is designed to eliminate the interference of complex background in the image. The pose of the camera is recovered from the visible images of the power equipment based on Structure From Motion (SFM). Finally, the NeRF is employed to complete the 3D reconstruction of the visible model and temperature field model of power equipment, enabling 3D fusion perception of the 2D heterogenous monitoring data. Experimental results show that this method can realize high-fidelity 3D reconstruction of power equipment within 30 s, which is superior to existing methods in terms of accuracy, modeling speed, automation degree, robustness and lightweight.