Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

Understand

Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility.

• They have also been shown to be effective inverse problem solvers, acting as the prior of the distribution, while the information of the forward model can be granted at the sampling stage.
• Nonetheless, as the generative process remains in the same high dimensional (i.e.
• identical to data dimension) space, the models have not been extended to 3D inverse problems due to the extremely high memory and computational cost.