title: Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
summary: 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 and models will be 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 .
paper_authors._id: 65ea78fe570481e38ef61d85
paper_authors.name: Yuanhao Cai
paper_authors._id: 65ea78fe570481e38ef61d86
paper_authors.name: Yixun Liang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-08T08:33:19.725Z
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paper_authors.fullname: yixunliang
paper_authors.type: user
paper_authors.user: lyxun
paper_authors._id: 65ea78fe570481e38ef61d87
paper_authors.name: Jiahao Wang
paper_authors._id: 65ea78fe570481e38ef61d88
paper_authors.name: Angtian Wang
paper_authors._id: 65ea78fe570481e38ef61d89
paper_authors.name: Yulun Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-08T09:53:38.863Z
paper_authors._id: 647a0400bfaa9e96b8529a7f
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paper_authors.fullname: Yulun Zhang
paper_authors.type: user
paper_authors.user: yulunzhang
paper_authors._id: 65ea78fe570481e38ef61d8a
paper_authors.name: Xiaokang Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-08T09:53:50.914Z
paper_authors._id: 64eaba6b1d8b651a9aed1203
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paper_authors.fullname: yangxiaokang
paper_authors.type: user
paper_authors.user: zicheqingluo
paper_authors._id: 65ea78fe570481e38ef61d8b
paper_authors.name: Zongwei Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-08T09:53:57.876Z
paper_authors._id: 64da5ec2a21d6c044b8d4fdb
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paper_authors.fullname: Zongwei Zhou
paper_authors.type: user
paper_authors.user: MrGiovanni
paper_authors._id: 65ea78fe570481e38ef61d8c
paper_authors.name: Alan Yuille
paper_title: Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
paper_summary: 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 and models will be 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 .
paper_ai_summary: A 3D Gaussian splatting-based framework, X-Gaussian, is proposed for efficient X-ray novel view synthesis with faster training and inference compared to NeRF.
paper_ai_keywords: Gaussian splatting radiative Gaussian point cloud model Differentiable Radiative Rasterization (DRR) Angle-pose Cuboid Uniform Initialization (ACUI) sparse-view CT reconstruction