GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908480061833216 |
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| author | Yuluo, Yikuang Ma, Yue Shen, Kuan Jin, Tongtong Liao, Wang Ma, Yangpu Wang, Fuquan |
| author_facet | Yuluo, Yikuang Ma, Yue Shen, Kuan Jin, Tongtong Liao, Wang Ma, Yangpu Wang, Fuquan |
| contents | 3D Gaussian Splatting (3DGS) has emerged as a promising approach for CT reconstruction. However, existing methods rely on the average gradient magnitude of points within the view, often leading to severe needle-like artifacts under sparse-view conditions. To address this challenge, we propose GR-Gaussian, a graph-based 3D Gaussian Splatting framework that suppresses needle-like artifacts and improves reconstruction accuracy under sparse-view conditions. Our framework introduces two key innovations: (1) a Denoised Point Cloud Initialization Strategy that reduces initialization errors and accelerates convergence; and (2) a Pixel-Graph-Aware Gradient Strategy that refines gradient computation using graph-based density differences, improving splitting accuracy and density representation. Experiments on X-3D and real-world datasets validate the effectiveness of GR-Gaussian, achieving PSNR improvements of 0.67 dB and 0.92 dB, and SSIM gains of 0.011 and 0.021. These results highlight the applicability of GR-Gaussian for accurate CT reconstruction under challenging sparse-view conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction Yuluo, Yikuang Ma, Yue Shen, Kuan Jin, Tongtong Liao, Wang Ma, Yangpu Wang, Fuquan Image and Video Processing Computer Vision and Pattern Recognition 3D Gaussian Splatting (3DGS) has emerged as a promising approach for CT reconstruction. However, existing methods rely on the average gradient magnitude of points within the view, often leading to severe needle-like artifacts under sparse-view conditions. To address this challenge, we propose GR-Gaussian, a graph-based 3D Gaussian Splatting framework that suppresses needle-like artifacts and improves reconstruction accuracy under sparse-view conditions. Our framework introduces two key innovations: (1) a Denoised Point Cloud Initialization Strategy that reduces initialization errors and accelerates convergence; and (2) a Pixel-Graph-Aware Gradient Strategy that refines gradient computation using graph-based density differences, improving splitting accuracy and density representation. Experiments on X-3D and real-world datasets validate the effectiveness of GR-Gaussian, achieving PSNR improvements of 0.67 dB and 0.92 dB, and SSIM gains of 0.011 and 0.021. These results highlight the applicability of GR-Gaussian for accurate CT reconstruction under challenging sparse-view conditions. |
| title | GR-Gaussian: Graph-Based Radiative Gaussian Splatting for Sparse-View CT Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.02408 |