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.

DiSR-NeRF 是一个利用扩散引导的框架来实现视图一致的超分辨率 NeRF，该方法通过利用现有的强大 2D 超分辨率模型来规避高分辨率参考图像的要求，并通过 NeRF 的内在多视图一致性属性提出了迭代的 3D 同步技术来减轻 LR 渲染图像之间不一致的问题。通过 Renoised Score Distillation，我们进一步引入了一种新颖的用于 2D 图像分辨率的评分蒸馏目标函数。我们的 DiSR-NeRF 在合成和真实世界数据集上进行的定性和定量结果表明，与现有方法相比，我们的方法在 NeRF 超分辨率上可以取得更好的效果。