3D

TPDM improves 3D voxel generative modeling with 2D diffusion models. We show that 3D generative prior can be accurately represented as the product of two independent 2D diffusion priors that scale to both unconditional sampling and solving inverse problems.

We propose a method that can solve 3D inverse problems in the medical imaging domain using only the pre-trained 2D diffusion model augmented with the conventional model-based prior.