Diffusion based blockwise voxel representation and super resolution

Yulong Wang; Nayu Ding; Yujie Lu; Shen Cai

doi:10.1117/12.3038435

22 October 2024 Diffusion based blockwise voxel representation and super resolution

Yulong Wang, Nayu Ding, Yujie Lu, Shen Cai

Author Affiliations +

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 1327417 (2024) https://doi.org/10.1117/12.3038435
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yulong Wang, Nayu Ding, Yujie Lu, and Shen Cai "Diffusion based blockwise voxel representation and super resolution", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 1327417 (22 October 2024); https://doi.org/10.1117/12.3038435

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