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.
X射线由于其比自然光更强的穿透能力，被广泛应用于透射成像。在渲染新视角X射线投影时，现有方法主要基于NeRF，遭受长时间训练和慢速推理的问题。在本文中，我们提出了一个基于3D高斯Splatting的框架，命名为X-Gaussian，用于X射线新视角合成。首先，我们重新设计了一个辐射高斯点云模型，灵感来自X射线成像的各向同性特性。我们的模型在学习预测3D点的辐射强度时排除了视角方向的影响。基于此模型，我们开发了一个具有CUDA实现的可微分辐射栅格化（DRR）。其次，我们定制了一个角度-姿态立方体均匀初始化（ACUI）策略，直接使用X射线扫描器的参数计算相机信息，然后在包围扫描对象的立方体内均匀采样点位置。实验表明，我们的X-Gaussian在性能上超越了最先进的方法6.5 dB，同时享受不到15%的训练时间和超过73倍的推理速度。在稀疏视图CT重建上的应用也揭示了我们方法的实际价值。