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 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 .
65ea78fe570481e38ef61d85
Yuanhao Cai
65ea78fe570481e38ef61d86
Yixun Liang
claimed_verified
2024-03-08T08:33:19.725Z
647d8f65becb41a272907e7a
/avatars/f891a1986a9361b23b7c83c23031cf26.svg
yixunliang
user
lyxun
65ea78fe570481e38ef61d87
Jiahao Wang
65ea78fe570481e38ef61d88
Angtian Wang
65ea78fe570481e38ef61d89
Yulun Zhang
admin_assigned
2024-03-08T09:53:38.863Z
647a0400bfaa9e96b8529a7f
/avatars/41161df5af1e6818d9e021389db316dd.svg
Yulun Zhang
user
yulunzhang
65ea78fe570481e38ef61d8a
Xiaokang Yang
admin_assigned
2024-03-08T09:53:50.914Z
64eaba6b1d8b651a9aed1203
/avatars/a0f26095c262ca1a204c27ff4bfb188c.svg
yangxiaokang
user
zicheqingluo
65ea78fe570481e38ef61d8b
Zongwei Zhou
admin_assigned
2024-03-08T09:53:57.876Z
64da5ec2a21d6c044b8d4fdb
https://cdn-avatars.huggingface.co/v1/production/uploads/64da5ec2a21d6c044b8d4fdb/FcWmea3_YSbUpLNb9YAWp.jpeg
Zongwei Zhou
user
MrGiovanni
65ea78fe570481e38ef61d8c
Alan Yuille
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 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 .
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.
Gaussian splatting radiative Gaussian point cloud model Differentiable Radiative Rasterization (DRR) Angle-pose Cuboid Uniform Initialization (ACUI) sparse-view CT reconstruction