3D Gaussian radiation field modeling for real-scene bridges

doi:10.11947/j.AGCS.2024.20240071

Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (9): 1694-1705.doi: 10.11947/j.AGCS.2024.20240071

• Precision Engineering Survey • Previous Articles

3D Gaussian radiation field modeling for real-scene bridges

Wei MA¹^,²^,³(), Qiang TU¹, Jianping PAN¹^,²^,³(), Lidu ZHAO¹, Wei TU⁴^,⁵^,⁶^,⁷, Qingquan LI⁴^,⁵^,⁶^,⁷

^1.College of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
^2.Key Laboratory of Land Space Planning Monitoring, Evaluation, and Early Warning, Ministry of Natural Resources, Chongqing 400074, China
^3.Engineering Technology Innovation Center for Smart City Spatiotemporal Information and Equipment, Ministry of Natural Resources, Chongqing 401121, China
^4.Department of Urban Spatial Information Engineering, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
^5.Key Laboratory of Intelligent Perception and Services for Spatial Information, Shenzhen 518060, China
^6.Key Laboratory of Urban Spatial Information Engineering of Guangdong Province, Shenzhen 518060, China
^7.Key Laboratory of Geographic Environment Monitoring in the Greater Bay Area, Ministry of Natural Resources, Shenzhen 518060, China

Received:2024-02-21 Published:2024-10-16
Contact: Jianping PAN E-mail:weima@cqjtu.edu.cn;panjianping@qq.com;panJianping@qq.com
About author:MA Wei (1987—), male, PhD, associate professor, majors in intelligent remote sensing methods and applications, spatiotemporal big data engineering. E-mail: weima@cqjtu.edu.cn
Supported by:
The Open Fund of the Key Laboratory of Monitoring, Evaluation, and Early Warning of Territorial Spatial Planning, Ministry of Natural Resources(LMEE-KF2023004);The Open Fund of the Engineering Technology Innovation Center for Smart City Spatio-temporal Information and Equipment, Ministry of Natural Resources(STIEIC-KF202305);The National Natural Science Foundation of China(42001324);The Science and Technology Research Project of Chongqing Education Commission(KJQN202200744);Chongqing Natural Science Foundation(cstc2021jcyj-msxmX1147);The Key Research and Development Program of Ningxia Hui Autonomous Region(2022CMG02014);Chongqing Graduate Joint Training Base Construction Project(JDLHPYJD2019004)

Abstract

Abstract:

Realistic 3D modeling and digital twins have become essential foundations for bridge operation and management. However, given the complex geometric structures of bridges, current 3D modeling methods face issues such as large amounts of raw data collection, low modeling efficiency, and missing or deformed model details. In response to these challenges, this paper investigates a bridge realistic 3D reconstruction method based on 3D Gaussian radiance fields. This method utilizes 3D Gaussian functions to construct a Gaussian radiance field from sparse point clouds generated by captured images. Adaptive optimization of radiance field parameters is performed based on stochastic gradient descent, and real-time visualization of the 3D model is achieved through differentiable rasterization, resulting in high-quality bridge 3D reconstruction and rendering. The study explores the impact of different image resolutions and various parameter changes on bridge modeling. Comparisons with traditional methods are made to provide theoretical and technical support for further bridge applications, promoting efficient and accurate realistic 3D reconstruction of complex bridge structures.

Key words: 3D Gaussian splatting, 3D bridge reconstruction, stochastic gradient descent, differentiable rasterizer rendering

CLC Number:

P258

Wei MA, Qiang TU, Jianping PAN, Lidu ZHAO, Wei TU, Qingquan LI. 3D Gaussian radiation field modeling for real-scene bridges[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(9): 1694-1705.

Figures/Tables 13

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λ	PSNR↑	SSIM↑	LPIPS↓
0	27.154	0.815	0.363
0.1	27.775	0.837	0.324
0.2	27.908	0.843	0.310
0.3	27.892	0.841	0.306
0.4	27.671	0.841	0.303
0.5	27.659	0.840	0.300
0.6	27.278	0.835	0.301
0.7	27.118	0.831	0.302
0.8	27.047	0.831	0.301
0.9	26.866	0.829	0.302
1.0	26.248	0.822	0.305

迭代次数	PSNR↑	SSIM↑	LPIPS↓	训练时间	Loss
6000	24.197	0.747	0.459	3 min 8 s	0.079
12 000	25.710	0.794	0.389	7 min 32 s	0.047
18 000	26.338	0.813	0.359	13 min 1 s	0.048
24 000	26.916	0.820	0.347	18 min 28 s	0.039
30 000	27.454	0.824	0.339	23 min 18 s	0.041
36 000	27.349	0.824	0.339	28 min 45 s	0.038

3D Gaussian radiation field modeling for real-scene bridges

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