TunnelSDF-FilterNet: A Domain-Aware Neural Framework for High-Fidelity 3D Reconstruction of Railway Tunnels
Year: 2026
Type: article
Abstract: Accurate 3D reconstruction of railway tunnels is crucial for infrastructure maintenance, safety assessment, and digital twin development. However, existing methods often fail in real-world scenarios due to sensor noise and spurious geometry generation in non-structural ground regions—particularly caused by rails, sleepers, and ballast. To address these domain-specific challenges, we propose TunnelSDF-FilterNet, a domain-aware neural signed distance function (Neural SDF) framework explicitly designed for high-fidelity railway tunnel reconstruction. Our approach introduces three key innovations: (1) a robust preprocessing pipeline that integrates DBSCAN–RANSAC clustering with normal and height-based geometric filtering to automatically remove ground artifacts without manual intervention; (2) a tunnel-tailored Neural SDF training objective featuring a novel ground suppression loss to constrain reconstruction within the tunnel envelope and an edge-aware regularization term to preserve fine structural details such as segment joints; and (3) a NeuralPull-based surface refinement strategy applied during inference to achieve sub-voxel precision in surface extraction. Extensive experiments on real-world tunnel datasets demonstrate that TunnelSDF-FilterNet significantly outperforms state-of-the-art methods in both qualitative fidelity and quantitative metrics—including lower Chamfer Distance (CD), higher Normal Consistency (NC), reduced Ground Inclusion Rate (GIR), and improved F-Scores. The proposed framework delivers operationally deployable, high-precision 3D tunnel models, offering a practical AI-driven solution for intelligent railway infrastructure management and digital twin construction in modern railway systems.
Source: Urban Rail Transit
Institution: Beijing Jiaotong University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,590
OpenAlex ID: W7160718866