2026· IEEE Transactions on Computational Imaging· Vol 12, pp. 1241-1253· 0 citations· 45 references
Computer Science
Abstract
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.
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
Few-view surface reconstruction recovers the visible surfaces of a scene from a few posed RGB images, providing the 3D models that robots need to explore and interact online. On mobile platforms, the reconstruction must be fast and geometrically accurate while keeping a small memory footprint to ensure safe and efficient operation. 3D Gaussian Splatting (3DGS) offers a high-fidelity scene representation, but building it from a few views is ill-posed, as many distinct surfaces reproduce the same images, making traditional photometric methods prone to"floater"artifacts. End-to-end methods resolve the ambiguity by regressing splats with large, usually Transformer-based, networks that require heavy compute and memory while generalizing poorly to new scenes. We propose G2SR, which exploits a well-posed core of the task: given cross-view 2D splat correspondences, 3D splats follow analytically from multi-view geometry. G2SR employs a lightweight neural frontend to detect and track 2D Gaussian splats on the image plane and an analytic backend to triangulate each into a metric-scale 3D splat. On ScanNet, Replica, and DTU, G2SR matches or exceeds the geometric accuracy of state-of-the-art end-to-end methods while running at 69-89 reconstructions per second within 203 MB of GPU memory (5-107x less) for 2- and 3-view inputs at 384 x 512 resolution, offering a practical path to online Gaussian-based surface reconstruction.
Dasong Gao, Vivienne Sze, S. Karaman· arXiv.org· 0 citations
Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray projections is severely ill-posed. Recently, 3D Gaussian Splatting has achieved state-of-the-art performance in sparse-view reconstruction by representing the volume using explicit, optimized primitives, but it requires dozens of projected views. With fewer views, reconstruction quality degrades severely since the explicit primitives are optimized freely without any anatomical information. Anatomical structures, in contrast, share similar geometry and density across a population. Their variations are bounded within a limited range that statistical shape models can capture. This paper proposes a shape-guided Gaussian splatting framework for sparse-view X-ray 3D reconstructions. Our contribution lies in driving Gaussian positions toward anatomically valid configurations, alongside atlas-based density regularization. Our method ensures anatomically consistent reconstruction and improves PSNR by 2.83 dB over a state-of-the-art Gaussian splatting baseline with as few as 5 views. Code Available: https://github.com/polyshape-lab/ShapeGuidedGaussian
Pranav Poudel, Florence Dell'Aniello Picard, N. Shehata et al.· 0 citations
Sparse voxel reconstruction offers an efficient representation for high-fidelity 3D modeling, yet its geometry is commonly optimized from local photometric evidence and discrete visibility statistics. This often leads to fragmented surfaces, excessive subdivision, and floating artifacts, particularly in weakly textured or sparsely observed regions. We introduce SurfSVR, a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers. Instead of directly lifting noisy pixel-wise depth predictions, SurfSVR first organizes each image into coherent surface regions by jointly reasoning over appearance, monocular depth, normals and cross-view geometry. Each region is then represented by an adaptively selected planar or quadratic surface model based on fitting reliability and geometric complexity, while cross-model agreement distinguishes reliable geometry from ambiguous predictions. These structured 2D priors are lifted into 3D and integrated throughout the reconstruction pipeline. They guide surface-adaptive voxel subdivision, provide region-level depth and normal supervision during optimization, enhance geometrically reliable sparse-observed surfaces in voxel pruning, and suppress off-surface floaters during post-refinement training. This unified design converts semantic and geometric coherence in image space into persistent structural constraints in 3D. Extensive experiments on 3 public benchmarks demonstrate that SurfSVR consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics, achieving state-of-the-art reconstruction quality. Codes and models will be released soon.
Yan Di, Chengxi Li, Yaoxing Wang et al.· 0 citations
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
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.
Yuxuan Song, Fan Gao, Yi-Bo Zhao et al.· 0 citations
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