The reconstruction method for static exterior model of digital twin railway station based on mobile vehicle

Constructing a static exterior model of railway passenger station is a preliminary and crucial step in achieving a digital twin station. Station modeling often relies on manual techniques, requiring significant labor for stations spanning tens of thousands of square meters. While the interior decoration is symmetrical, the asymmetrical placement of equipment often leads to confusion in manual modeling. This paper proposes a novel station static model reconstruction method - MSCRAGS (Mobile vehicle-Sparse Sampling-Colmap-Resolution adjustment-Gaussian Splatting). The MSCRAGS integrates the requirements for flexible control of static exterior models by utilizing mobile vehicle for data collection and incorporates the sparse multi-view spatial sampling approach. It involves collecting multi-height and multi-angle appearance color data of passenger operation elements to construct preliminary point clouds. Subsequently, 3d Gaussian splatting is employed for rendering, achieving high-fidelity reconstruction of production elements. Moreover, according to the requirements for the precision control of static exterior models, the rendering effects are reshaped with resolution adjustment to obtain static exterior models of station production elements at various resolutions. Experiments conducted at Qinghe Station demonstrate that compared to other state-of-art modeling methods, our approach significantly reduces modeling time and improves modeling accuracy, showing superior performance in modeling high-fidelity indices.

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Publication Details

Journal
Scientific Reports
Published
2025-04-24
DOI
https://doi.org/10.1038/s41598-025-96535-0
Primary Topic
Simulation and Modeling Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

The reconstruction method for static exterior model of digital twin railway station based on mobile vehicle

Wei Bai, Xin Wang, Xiaojun Lv, Tianyun Shi et al.
Scientific Reports
Simulation and Modeling Applications
article

The reconstruction method for static exterior model of digital twin railway station based on mobile vehicle

Wei Bai, Xin Wang, Xiaojun Lv, Tianyun Shi, Lingbin Bu, Kaibei Peng
article en

Abstract

Constructing a static exterior model of railway passenger station is a preliminary and crucial step in achieving a digital twin station. Station modeling often relies on manual techniques, requiring significant labor for stations spanning tens of thousands of square meters. While the interior decoration is symmetrical, the asymmetrical placement of equipment often leads to confusion in manual modeling. This paper proposes a novel station static model reconstruction method - MSCRAGS (Mobile vehicle-Sparse Sampling-Colmap-Resolution adjustment-Gaussian Splatting). The MSCRAGS integrates the requirements for flexible control of static exterior models by utilizing mobile vehicle for data collection and incorporates the sparse multi-view spatial sampling approach. It involves collecting multi-height and multi-angle appearance color data of passenger operation elements to construct preliminary point clouds. Subsequently, 3d Gaussian splatting is employed for rendering, achieving high-fidelity reconstruction of production elements. Moreover, according to the requirements for the precision control of static exterior models, the rendering effects are reshaped with resolution adjustment to obtain static exterior models of station production elements at various resolutions. Experiments conducted at Qinghe Station demonstrate that compared to other state-of-art modeling methods, our approach significantly reduces modeling time and improves modeling accuracy, showing superior performance in modeling high-fidelity indices.

Scientific ReportsVol. 15(1)
China People's Public Security University (CN), Institute of Computing Technology (CN), China Academy of Railway Sciences (CN)
China Railway, National Natural Science Foundation of China, China Academy of Railway Sciences
Sustainable cities and communities
Openalex Percentile: Top 91%
Simulation and Modeling Applications
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