Aug 2026· Visual Computing for Industry, Biomedicine, and Art· Vol 9· 0 citations· 39 references
Medicine
TL;DR
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.
Abstract
Novel view synthesis, which is essential for three-dimensional (3D) reconstruction, traditionally requires dense image sets and pre-calibrated camera parameters. However, in industrial digital twin applications spatial constraints often limit data acquisition to sparse views that fail to provide the feature correspondences necessary for accurate modeling. To overcome this bottleneck, a semantic-guided 3D Gaussian splatting (3DGS) framework tailored to sparse-view industrial reconstruction was introduced. Rather than relying on precise initialization and dense inputs, the proposed method couples explicit 3D Gaussian representations with the dense and unconstrained stereo 3D reconstruction (DUSt3R) end-to-end pose estimation model. The severe occlusions, typical of factory environments, are handled by incorporating Segment Anything Model 2 (SAM2) to hierarchically decompose the scene, yielding a structured representation that improves optimization stability. A probability density field-driven algorithm is subsequently applied to extract lightweight meshes directly from the optimized Gaussian point clouds. Evaluations on the MipNeRF360 benchmark and a custom industrial dataset demonstrated substantial improvements in the structural similarity index measure. By enabling robust reconstruction from limited viewpoints, this pipeline offers a practical geometric foundation for automated inspection and remote equipment monitoring.
3D Gaussian Splatting has become a main technique for fast 3D scene reconstruction and editing, leveraging an efficient and flexible explicit representation for high-fidelity real-time rendering. However, the quality of the point clouds used to initialize Gaussians remains a key factor that limits fine-grained geometry reconstruction. To address this limitation, we propose an Iterative Spatial Decomposition (ISD) framework that bridges dense geometric priors from Multi-View Stereo (MVS) with Gaussian Splatting. ISD mitigates the mismatch between dense MVS point clouds and Gaussian sparsity by iteratively partitioning the scene into voxels of adaptive granularity and performing density-aware point assignment. Building on ISD, we introduce Hierarchical Geometric Prior Sampling (HGPS) to substantially reduce redundancy in MVS point clouds while preserving critical details, thereby providing a more robust geometric foundation for reconstruction. We further develop Hierarchical Geometry-aware Initialization (HGI), which uses a voxel-radius-based adaptive parameter initialization and replaces the iterative KNN-based procedure with batch computation, enabling efficient and robust Gaussian initialization. Additionally, we propose a Hierarchical Geometry-aware Densification (HGD) method. By dynamically identifying over-reconstructed or under-reconstructed regions through voxel constraints, HGD enhances detail reconstruction quality while controlling storage overhead. Extensive experiments on Mip‑NeRF360, Tanks & Temples, and Deep Blending demonstrate significant improvements in rendering quality, achieving state-of-the-art LPIPS performance. These results indicate that our approach effectively alleviates deficiencies in the geometric priors of initial point clouds and recovers richer geometric details.
Zonghua Yu, Junhuai Li, Huaijun Wang et al.· ACM Transactions on Multimed...· 0 citations
Reconstructing high-fidelity 3D scenes from sparse-views remains a central problem in generalizable neural rendering. Existing generalizable 3D Gaussian Splatting (3DGS) methods often exhibit geometric artifacts in sparse-view settings, since supervision based solely on 2D photometric losses cannot resolve depth and correspondence ambiguities. To address this issue, we propose MAC-Splat, a training framework built around direct 3D consistency supervision. MAC-Splat builds on the MASt3R geometric backbone and a frozen DINOv3 encoder to obtain semantically informed 2D correspondences, which serve as geometric anchors for 3D supervision. Using these anchors, we define the Multi-Attribute Consistency (MAC) loss. This objective jointly regularizes the 3D attributes of matched Gaussians, including their position, shape, and appearance, by enforcing agreement in a common world coordinate frame. The formulation is robust to outliers and respects the geometry of covariance matrices, which leads to stable training under sparse-view conditions. Experiments on ScanNet++ show that MAC-Splat outperforms strong baselines, with particularly large gains under different overlap regimes. In particular, it improves average PSNR over Splatt3R by more than 4.5 dB, reduces LPIPS, and maintains performance as the camera pose gap increases. These results indicate that a direct, multi-attribute 3D consistency objective, when combined with high-quality correspondences, is effective for addressing the ill-posed sparse-view reconstruction problem.
Jinqian Yang, Yichen Wu, Wanhua Li et al.· 1 citation
Recent advances in 3D Gaussian Splatting (3DGS) have significantly advanced indoor scene reconstruction, a key capability for AR/VR, robotics, and autonomous systems. However, existing methods that rely on 2D monocular priors often produce artifacts such as holes and unnatural protrusions due to insufficient or weak constraints. To address this limitation, we propose a geometry-semantics co-regularization framework that jointly optimizes geometry and semantics within 3DGS. On the geometric side, we introduce a multi-level geometric optimization strategy with two key contributions. First, inspired by neural implicit surface representations, we design a dual implicit architecture that integrates Implicit Moving Least Squares (IMLS) and Signed Distance Functions (SDFs). This structure enables mutual reinforcement between the two representations via local geometric approximation and global normal constraints, improving fine-grained surface modeling. Second, we develop a view-dependent normal consistency constraint that combines 2D observations with SDF gradients to refine surface accuracy. On the semantic side, we develop a multi-view semantic consistency supervision to regularize the semantic distributions of Gaussian primitives, ensuring cross-view consistency for Gaussians corresponding to the same semantic category or instance. Extensive experiments on the Replica and MuSHRoom datasets demonstrate that our approach achieves state-of-the-art geometric reconstruction quality and rendering fidelity. Check out the project page https://sgo-gauss.github.io for the source code and more results.
Haihong Xiao, Jianan Zou, Yanan Zhang et al.· IEEE Transactions on Visuali...· 0 citations
3D Gaussian Splatting enables efficient novel view synthesis, but accurate mesh reconstruction remains difficult in weakly observed and occluded regions, where Gaussian primitives may grow into unstable or geometrically inconsistent structures. We propose CoMVS-GS, a general surface reconstruction framework that combines Multi-View Stereo with Gaussian splatting. CoMVS-GS initializes Gaussian primitives from dense multi-view stereo points with pre-flattened scales and normal-aligned orientations, providing stronger geometric priors than sparse structure-from-motion initialization and reducing ambiguity during early optimization. It further introduces PatchMatch-3DGS Mutual Supervision, where Gaussian-rendered depths and normals initialize PatchMatch refinement, and refined PatchMatch depths supervise Gaussian optimization to improve weakly constrained geometry. For surface extraction, CoMVS-GS replaces truncated signed distance field voxel fusion with a Delaunay graph-cut meshing pipeline, reducing sensitivity to voxel resolution while preserving visibility-consistent surface evidence. Experiments on DTU, GauU-Scene V2, and MatrixCity show that CoMVS-GS remains competitive on object-level reconstruction and improves geometric accuracy and mesh compactness in outdoor scenes while maintaining high rendering quality.
Shihan Chen, Junjing Zhang, Qingsong Yan et al.· 0 citations
SARG-GS is proposed, a geometry-driven 3DGS framework tailored for sparse-view scenarios, comprising a Semantic Augmented Epipolar Fusion (SAEF) module and a Residual Guided Reprojection Compensation (RRC) module, which achieves superior structural completeness and rendering fidelity with as few as three input views.
Huan Zhou, Huizhi Zhu, Jiongming Qin et al.· The Visual Computer· 0 citations
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.
Jinhua Qian, Weifeng Wei, Fei Xue et al.· Journal of computing in civi...· 0 citations