  3D Gaussian splatting (3DGS) has shown promising results in image rendering
and surface reconstruction. However, its potential in volumetric reconstruction
tasks, such as X-ray computed tomography, remains under-explored. This paper
introduces R2-Gaussian, the first 3DGS-based framework for sparse-view
tomographic reconstruction. By carefully deriving X-ray rasterization
functions, we discover a previously unknown integration bias in the standard
3DGS formulation, which hampers accurate volume retrieval. To address this
issue, we propose a novel rectification technique via refactoring the
projection from 3D to 2D Gaussians. Our new method presents three key
innovations: (1) introducing tailored Gaussian kernels, (2) extending
rasterization to X-ray imaging, and (3) developing a CUDA-based differentiable
voxelizer. Extensive experiments demonstrate that our method outperforms
state-of-the-art approaches by 0.93 dB in PSNR and 0.014 in SSIM. Crucially, it
delivers high-quality results in 3 minutes, which is 12x faster than NeRF-based
methods and on par with traditional algorithms. The superior performance and
rapid convergence of our method highlight its practical value.
