  Neural Radiance Fields (NeRF) have achieved great success in the task of
synthesizing novel views that preserve the same resolution as the training
views. However, it is challenging for NeRF to synthesize high-quality
high-resolution novel views with low-resolution training data. To solve this
problem, we propose a zero-shot super-resolution training framework for NeRF.
This framework aims to guide the NeRF model to synthesize high-resolution novel
views via single-scene internal learning rather than requiring any external
high-resolution training data. Our approach consists of two stages. First, we
learn a scene-specific degradation mapping by performing internal learning on a
pretrained low-resolution coarse NeRF. Second, we optimize a super-resolution
fine NeRF by conducting inverse rendering with our mapping function so as to
backpropagate the gradients from low-resolution 2D space into the
super-resolution 3D sampling space. Then, we further introduce a temporal
ensemble strategy in the inference phase to compensate for the scene estimation
errors. Our method is featured on two points: (1) it does not consume
high-resolution views or additional scene data to train super-resolution NeRF;
(2) it can speed up the training process by adopting a coarse-to-fine strategy.
By conducting extensive experiments on public datasets, we have qualitatively
and quantitatively demonstrated the effectiveness of our method.
