  Recent work on Neural Radiance Fields (NeRF) exploits multi-view 3D
consistency, achieving impressive results in 3D scene modeling and
high-fidelity novel-view synthesis. However, there are limitations. First,
existing methods assume enough high-quality images are available for training
the NeRF model, ignoring real-world image degradation. Second, previous methods
struggle with ambiguity in the training set due to unmodeled inconsistencies
among different views. In this work, we present RustNeRF for real-world
high-quality NeRF. To improve NeRF's robustness under real-world inputs, we
train a 3D-aware preprocessing network that incorporates real-world degradation
modeling. We propose a novel implicit multi-view guidance to address
information loss during image degradation and restoration. Extensive
experiments demonstrate RustNeRF's advantages over existing approaches under
real-world degradation. The code will be released.
