High-Fidelity Gaussian Splatting from MVS Clouds: An Iterative Spatial Decomposition framework

Semantic-guided 3D Gaussian splatting for sparse-view reconstruction in industrial digital twins

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.

GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View Tomographic Reconstruction

Computed tomography (CT) reconstruction under sparse-view acquisition is fundamentally ill-posed. Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative to implicit neural fields for per-case tomographic reconstruction, offering explicit geometric primitives and fast differentiable rendering. However, under sparse projection supervision, existing 3DGS-based CT methods can become unstable because the Gaussian primitives are optimized largely independently, often leading to overfitting and needle-like artifacts. In this work, we propose GR-Gaussian, a graph-regularized radiative 3DGS framework for sparse-view CT reconstruction. Rather than introducing a new volumetric representation, our method augments radiative Gaussian optimization with local graph-guided structural cues. Specifically, we introduce two components: (1) a denoised point-cloud initialization strategy (De-Init), which filters artifact-contaminated FDK priors to provide a more reliable initialization for Gaussian placement and neighborhood construction; and (2) a Pixel-Graph-Aware (PGA) densification criterion, which supplements the baseline pixel-aware densification signal with local density contrast measured on the Gaussian neighborhood graph. In addition, we incorporate graph Laplacian and volumetric total variation regularization to improve structural consistency during optimization. Experiments on the X-3D and real-world CT datasets show that GR-Gaussian consistently improves reconstruction quality over the evaluated baselines, while providing cleaner structures and stronger suppression of sparse-view artifacts. These results indicate that initialization and graph-guided densification are effective practical extensions to radiative 3DGS for sparse-view CT reconstruction.

Edge-aware gaussian splatting: enhancing boundary fidelity in 3D scene rendering

UH-GS: Uncertainty-aware hierarchical Gaussian splatting for outdoor scene reconstruction

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.

Depth-Aware Gaussian Splatting with Dynamic Masking for High-Fidelity Geometric Modeling of Shield Tunnel Digital Twins

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.

AnyGS2Mesh: Feed-Forward Mesh Reconstruction from 3D Gaussian Splatting with Arbitrary-Resolution Views

Existing 3D mesh reconstruction methods from Gaussian scene representations predominantly rely on iterative optimization, resulting in slow inference and limited scalability to high-resolution inputs. In this paper, we present AnyGS2Mesh, the first feed-forward framework for directly reconstructing 3D meshes from 3D Gaussian Splatting representations with support for arbitrary input image resolutions. Our approach incorporates a Gaussian-Guided Transformer architecture that exploits explicit 3D geometric priors for efficient mesh generation. We introduce three key components: (1) a Gaussian-Guided Spatial Reasoning Transformer represents Gaussian primitives as structured 3D tokens and jointly reasons over Gaussian and image features; (2) a Streaming and Patchwise Geometry Encoder processes native-resolution views sequentially and aggregates information across variable-length view sets; (3) a Scale-Aligned Hybrid Depth Refiner uses a PatchFusion-style encoder--decoder to fuse RGB-conditioned predicted depth with Gaussian-rendered metric depth, combining fine local structures with globally consistent metric scale. The refined depth maps are integrated through TSDF fusion, followed by Marching Cubes for deterministic mesh extraction. Extensive experiments show that AnyGS2Mesh achieves state-of-the-art reconstruction quality while significantly reducing inference time compared with optimization-based baselines, enabling near-real-time, high-quality mesh reconstruction. Our results demonstrate the potential of combining Gaussian representations and feed-forward Transformer architectures for scalable 3D geometry reconstruction. The code will be made publicly available upon acceptance.