Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework
Yi-Ting Chen, Ting-Hsuan Liao, Pengsheng Guo, Alexander Schwing, Jia-Bin Huang
Abstract
We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released.
BibTeX
@InProceedings{Chen_2025_ICCV,
author = {Chen, Yi-Ting and Liao, Ting-Hsuan and Guo, Pengsheng and Schwing, Alexander and Huang, Jia-Bin},
title = {Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13481-13490}
}