Diffusion models have become a popular approach for image generation and
reconstruction due to their numerous advantages. However, most diffusion-based
inverse problem-solving methods only deal with 2D images, and even recently
published 3D methods do not fully exploit the 3D distribution prior. To address
this, we propose a novel approach using two perpendicular pre-trained 2D
diffusion models to solve the 3D inverse problem. By modeling the 3D data
distribution as a product of 2D distributions sliced in different directions,
our method effectively addresses the curse of dimensionality. Our experimental
results demonstrate that our method is highly effective for 3D medical image
reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing
MRI, and sparse-view CT. Our method can generate high-quality voxel volumes
suitable for medical applications.Comment: ICCV23 poster. 15 pages, 9 figure