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.

本文介绍了第一个基于 3D 高斯喷洒（3DGS）的稀疏视图层析重建框架 R2-Gaussian，通过从 3D 到 2D 高斯的重投影，引入了定制高斯核、扩展到 X 射线成像以及基于 CUDA 的可微体素化器等三个关键创新，通过大量实验证明，我们的方法在 PSNR 和 SSIM 方面优于最先进的方法 0.93 dB 和 0.014，关键是能够在 3 分钟内产生高质量结果，比 NeRF 方法快 12 倍，并与传统算法持平，凸显出其实用价值。