A Three-Dimensional Model Reconstruction Method of Sub‍station Based on Structure-Aware 3D Gaussian Splatting

doi:10.3969/j.issn.1008-0198.2025.02.015

Abstract

Abstract: Aiming at the problem that the point cloud complementation technologies or three-dimensional (3D) scanning techniques currently used in 3D model reconstruction of substations rely on high-precision lidar hardware and can only reconstruct moderately sparse and visually inaccurate 3D models, the dual-branch 3D modeling network based on visible light images is proposed, which greatly reduces the dependency on hardware equipment. By fully exploiting the advantages of signed distance fields and 3D Gaussian splatting through mutual geometric supervision and bidirectional guidance, it can efficiently reconstruct texture-rich, dense, and visually high-fidelity substation scenes and their equipment models.

Key words: transformer substation, 3D model reconstruction, 3D Gaussian splatting

CLC Number:

TM63

KANG Wen, DING Yuzhu, DENG Wei, ZHONG Yuming, TANG Jiangqi. A Three-Dimensional Model Reconstruction Method of Sub‍station Based on Structure-Aware 3D Gaussian Splatting[J]. Hunan Electric Power, 2025, 45(2): 110-115.

References

[1] 马彦彰,李先允. 变电站数字孪生体的SFM三维建模方法[J] .建筑结构,2023,53(7):169.
[2] 郑文杰,乔木,杨袆,等. 基于点云数据的变电站三维建模方法研究[J]. 山东电力技术,2024,51(3):65-72.
[3] 陈志辉,甘运良,董跃周,等.基于分布式快速存储的变电站三维建模数据处理技术研究[J]. 电子设计工程,2023,31(2):130-134.
[4] 薛江,郭建龙,冯伟夏,等. 基于改进SSD神经网络和点云配准算法的变电站三维模型快速建立方法[J].武汉大学学报(工学版),2024,57(3):356-362.
[5] 范子恺,王菲,任佳依,等. 基于数字孪生技术的变电站快速建模点云分割方法[J]. 微型电脑应用,2024,40(3):117-120.
[6] 常龙邦. 基于信息化模型的变电站三维建模研究[J]. 电气技术与经济,2023(7):161-163,175.
[7] 潘玉超. 基于智能管控系统和BIM的特高压变电站自动化三维建模[J]. 自动化应用,2024,65(4):160-162,165.
[8] 弓国军,符国晖,周亚敏.基于BIM技术的变电站三维可视化建模研究[J]. 电子制作,2022,30(2):66-68.
[9] 叶子玉. 基于三维激光扫描技术的变电站精细建模技术研究与应用[J]. 农村电气化,2023(2):35-39.
[10] 党杨梅,赵成明. 三维激光扫描技术在变电站三维建模中的应用[J]. 济源职业技术学院学报,2021,20(3):62-66.
[11] 吴兆彬,王程,陈维,等. 数字孪生技术在变电站三维实景建模中的应用[J]. 微型电脑应用,2024,40(6):226-229.
[12] 张磊,章剑光,赏炜,等. 基于GIM的变电站模型轻量化三维展示研究[J]. 微型电脑应用,2024,40(3):125-129.
[13] 张帅,张正鹏,刘树辉,等.一种电网信息模型的三维重构与优化方法[J]. 测绘科学,2022,47(3):192-201.
[14] 白汗章,郭科,常亮. 基于GIM技术和YOLOv3深度网络的三维电网建模及校验方法[J]. 沈阳工业大学学报,2023,45(3):247-252.
[15] 杨梓瀚. 一种基于倾斜摄影的变电站三维实景建模方法[J]. 电子测试,2022,36(9):126-128.
[16] LI R H,GAN L,LIU Y,et al.A model-driven approach for fast modeling of three-dimensional laser point cloud in large substation[J]. Scientific Reports,2023,13(1):16092.
[17] KERBL B,KOPANAS G,LEIMKÜHLER T,et al. 3D Gaussian splatting for real-time radiance field rendering[J]. ACM Transaction on Graphics,2023,42(4):1-14.
[18] MILDENHALL B,SRINIVASAN P P,TANCIK M,et al.NeRF:representing scenes as neural radiance fields for view synthesis[C]//Computer Vision-ECCV 2020. Cham:Springer International Publishing,2020:405-421.
[19] WANG P,LIU L,LIU Y,et al. NeuS:learning neural implicit surfaces by volume rendering for multi-view reconstruction[EB/OL].(2023-02-01)[2024-10-15]. https://arxiv.org/abs/2106.10689v3.
[20] CHEN A P,XU Z X,GEIGER A,et al.TensoRF:Tensorial radiance fields[C]//European conference on computer vision. Cham:Springer Nature Switzerland,2022:333-350.
[21] SCHONBERGER J L,FRAHM J M.Structure-from-motion revisited[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas,NV,USA. IEEE,2016:4104-4113.