Nov 2026· Journal of computing in civil engineering· Vol 40· 0 citations· 32 references
Computer Science
TL;DR
A two-dimensional Gaussian splatting (2DGS) modeling approach integrated with a dynamic depth-aware masking mechanism is introduced to guide the model to focus on near-field structural optimization under longitudinal viewing conditions and provides a novel technical paradigm for shield tunnel digital-twin geometric modeling.
Abstract
The construction of high-fidelity twin geometric models is essential for advancing tunnel digital-twin technology. Image-based three-dimensional (3D) reconstruction, which directly infers 3D scene structures from visual semantics, has demonstrated considerable potential. However, most existing approaches rely on the conventional structure-from-motion (SfM) and multiview stereo (MVS) pipeline, which often suffers from point cloud voids and texture blurring in shield tunnel environments with low-texture segments and dim lighting. In addition, the inherent discreteness of point cloud representations makes subsequent denoising and optimization inefficient. To address these limitations, this study proposes a two-dimensional (2D) Gaussian splatting (2DGS) modeling approach integrated with a dynamic depth-aware masking mechanism. Image data are efficiently captured from a tunnel-longitudinal viewpoint, and sparse point clouds reconstructed via SfM are parameterized using 2DGS to enable adaptive geometric reconstruction under image supervision. Considering the linear and long-distance geometric characteristics of shield tunnels, a depth-aware dynamic masking strategy is introduced to guide the model to focus on near-field structural optimization under longitudinal viewing conditions. The optimized Gaussian model is rendered into depth maps at each camera viewpoint and fused using a truncated signed distance function to generate the final shield tunnel digital-twin geometric model. Experimental results from real tunnel engineering scenarios show that the proposed method achieves a geometric accuracy error of only 0.7% compared with SfM+MVS methods, significantly alleviates voids and local blurring artifacts, produces clearer segment contours, and reduces modeling time by approximately 37%. The proposed approach provides a novel technical paradigm for shield tunnel digital-twin geometric modeling.
A semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced, enabling robust reconstruction from limited viewpoints and offers a practical geometric foundation for automated inspection and remote equipment monitoring.
Boyang Li, Tian-Han Gao, Zuan Gu et al.· Visual Computing for Industr...· 0 citations
This work proposes an Iterative Spatial Decomposition framework that bridges dense geometric priors from Multi-View Stereo (MVS) with Gaussian Splatting and introduces Hierarchical Geometric Prior Sampling (HGPS), which substantially reduce redundancy in MVS point clouds while preserving critical details, thereby providing a more robust geometric foundation for reconstruction.
Zonghua Yu, Junhuai Li, Huaijun Wang et al.· ACM Transactions on Multimed...· 0 citations
A 3D-GS-based framework that attaches a simplified Disney Bidirectional Reflectance Distribution Function (BRDF) and differentiable physically based rendering (PBR) shader to each surface Gaussian for interpretable material attributes delivers higher geometric accuracy, improved material realism, and real-time rendering performance suitable for applications such as Simultaneous Localization and Mapping (SLAM) and relighting.
Wei-Chen Xu, W. Wan, M. Peng· International Conference on...· 0 citations
Neural radiance fields (NeRF) and 3D Gaussian Splatting (3DGS) are popular techniques to reconstruct and render photorealistic images. However, the prerequisite of running Structure-from-Motion (SfM) to get camera poses limits their completeness. Although previous methods can reconstruct a few unposed images, they are not applicable when images are unordered or densely captured. In this work, we propose a method to train 3DGS from unposed images. Our method leverages a pre-trained 3D geometric foundation model as the neural scene representation. Since the accuracy of the predicted pointmaps does not suffice for accurate image registration and high-fidelity image rendering, we propose to mitigate the issue by initializing and fine-tuning the pre-trained model from a seed image. The images are then progressively registered and added to the training buffer, which is used to train the model further. We also propose to refine the camera poses and pointmaps by minimizing a point-to-camera ray consistency loss across multiple views. When evaluated on diverse challenging datasets, our method outperforms state-of-the-art pose-free NeRF/3DGS methods in terms of both camera pose
Yu Chen, Rolandos Alexandros Potamias, Evangelos Ververas et al.· Neural Information Processin...· 0 citations
Abstract. In outdoor scene reconstruction, dynamic occlusions and multiscale structures often undermine multiview consistency and hinder effective gradient accumulation of high-frequency Gaussian primitives, leading to artifacts and the loss of fine details in Gaussian splatting–based radiance field methods. To address these challenges, we propose an uncertainty-aware hierarchical Gaussian splatting framework for outdoor 3D reconstruction. Specifically, our method constructs a hierarchical octree-based spatial representation from the results of aerial triangulation. It introduces level of detail constraints to enable structured management and progressive optimization of Gaussian primitives across different scales. This design effectively alleviates the imbalance in training and the redundant growth of Gaussian primitives commonly observed in multiscale outdoor scenes. In addition, we incorporate an uncertainty prediction mechanism that evaluates the consistency between rendered results and ground-truth images in the feature space, allowing the model to automatically identify dynamically occluded regions and suppress their gradient contributions during optimization. As a result, the adverse impact of dynamic artifacts on static scene modeling is substantially reduced. Experimental results demonstrate that, without incurring significant additional training overhead, our method consistently improves structural consistency and fine-detail reconstruction quality in outdoor scenes, while simultaneously reducing model complexity and maintaining real-time rendering performance. Furthermore, the proposed approach can be seamlessly integrated into multiple mainstream Gaussian splatting frameworks, exhibiting strong robustness and promising potential for practical deployment.
Junxing Yang, Haoran Gao, Chun-Yu Huang et al.· Journal of Electronic Imagin...· 0 citations
This work proposes a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction and introduces a camera-conditioned geometric prior that guides the network toward geometrically grounded restoration that remains coherent across viewpoints.
Xinhui Liu, Can Wang, Wei Jiang et al.· 0 citations
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