  Neural rendering methods can achieve near-photorealistic image synthesis of
scenes from posed input images. However, when the images are imperfect, e.g.,
captured in very low-light conditions, state-of-the-art methods fail to
reconstruct high-quality 3D scenes. Recent approaches have tried to address
this limitation by modeling various degradation processes in the image
formation model; however, this limits them to specific image degradations. In
this paper, we propose a generalizable neural rendering method that can perform
high-fidelity novel view synthesis under several degradations. Our method,
GAURA, is learning-based and does not require any test-time scene-specific
optimization. It is trained on a synthetic dataset that includes several
degradation types. GAURA outperforms state-of-the-art methods on several
benchmarks for low-light enhancement, dehazing, deraining, and on-par for
motion deblurring. Further, our model can be efficiently fine-tuned to any new
incoming degradation using minimal data. We thus demonstrate adaptation results
on two unseen degradations, desnowing and removing defocus blur. Code and video
results are available at vinayak-vg.github.io/GAURA.
