DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF
Jie Long Lee Chen Li Gim Hee Lee
We present DiSR-NeRF, a diffusion-guided framework for view-consistent
super-resolution (SR) NeRF. Unlike prior works, we circumvent the requirement
for high-resolution (HR) reference images by leveraging existing powerful 2D
super-resolution models. Nonetheless, independent SR 2D images are often
inconsistent across different views. We thus propose Iterative 3D
Synchronization (I3DS) to mitigate the inconsistency problem via the inherent
multi-view consistency property of NeRF. Specifically, our I3DS alternates
between upscaling low-resolution (LR) rendered images with diffusion models,
and updating the underlying 3D representation with standard NeRF training. We
further introduce Renoised Score Distillation (RSD), a novel score-distillation
objective for 2D image resolution. Our RSD combines features from ancestral
sampling and Score Distillation Sampling (SDS) to generate sharp images that
are also LR-consistent. Qualitative and quantitative results on both synthetic
and real-world datasets demonstrate that our DiSR-NeRF can achieve better
results on NeRF super-resolution compared with existing works. Code and video
results available at the project website.