TunnelSDF-FilterNet: A Domain-Aware Neural Framework for High-Fidelity 3D Reconstruction of Railway Tunnels

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.