Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
<|reference_start|>Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis: X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on NeRF suffer from long training time and slow inference speed. In this paper, we propose a 3D Gaussian splatting-based framework, namely X-Gaussian, for X-ray novel view synthesis. Firstly, we redesign a radiative Gaussian point cloud model inspired by the isotropic nature of X-ray imaging. Our model excludes the influence of view direction when learning to predict the radiation intensity of 3D points. Based on this model, we develop a Differentiable Radiative Rasterization (DRR) with CUDA implementation. Secondly, we customize an Angle-pose Cuboid Uniform Initialization (ACUI) strategy that directly uses the parameters of the X-ray scanner to compute the camera information and then uniformly samples point positions within a cuboid enclosing the scanned object. Experiments show that our X-Gaussian outperforms state-of-the-art methods by 6.5 dB while enjoying less than 15% training time and over 73x inference speed. The application on sparse-view CT reconstruction also reveals the practical values of our method. Code is publicly available at https://github.com/caiyuanhao1998/X-Gaussian . A video demo of the training process visualization is at https://www.youtube.com/watch?v=gDVf_Ngeghg .<|reference_end|>
@article{cai2024radiative,
    title={Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis},
    author={Yuanhao Cai, Yixun Liang, Jiahao Wang, Angtian Wang, Yulun Zhang,
  Xiaokang Yang, Zongwei Zhou, Alan Yuille},
    journal={arXiv preprint arXiv:2403.04116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04116},
    primaryClass={eess.IV cs.CV}
}