R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction
<|reference_start|>R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction: 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.<|reference_end|>
@article{zha2024r$^2$-gaussian:,
    title={R$^2$-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic
  Reconstruction},
    author={Ruyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao, Yanhao Zhang, Hongdong
  Li},
    journal={arXiv preprint arXiv:2405.20693},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20693},
    primaryClass={eess.IV cs.CV}
}