Diffusion based blockwise voxel representation and super resolution

Neural implicit representation has emerged as a popular research direction within the realm of 3D deep learning, with a variety of implicit expression methods such as occupancy field, singed distance field (SDF), unsigned distance field (UDF), and NeRF being extensively employed in applications including 3D reconstruction. In this paper, we introduce an innovative blockwise high-resolution voxel representation and rough voxel super resolution technique based on diffusion models. We encode high-resolution voxel models using a set of latent vectors and reconstruct the original voxel models through the diffusion process. The experimental results validate that our approach achieves highly precise reconstruction outcomes in both voxel implicit representation and rough voxel super resolution tasks.