  Neural Radiance Field (NeRF) regresses a neural parameterized scene by
differentially rendering multi-view images with ground-truth supervision.
However, when interpolating novel views, NeRF often yields inconsistent and
visually non-smooth geometric results, which we consider as a generalization
gap between seen and unseen views. Recent advances in convolutional neural
networks have demonstrated the promise of advanced robust data augmentations,
either random or learned, in enhancing both in-distribution and
out-of-distribution generalization. Inspired by that, we propose Augmented NeRF
(Aug-NeRF), which for the first time brings the power of robust data
augmentations into regularizing the NeRF training. Particularly, our proposal
learns to seamlessly blend worst-case perturbations into three distinct levels
of the NeRF pipeline with physical grounds, including (1) the input
coordinates, to simulate imprecise camera parameters at image capture; (2)
intermediate features, to smoothen the intrinsic feature manifold; and (3)
pre-rendering output, to account for the potential degradation factors in the
multi-view image supervision. Extensive results demonstrate that Aug-NeRF
effectively boosts NeRF performance in both novel view synthesis (up to 1.5dB
PSNR gain) and underlying geometry reconstruction. Furthermore, thanks to the
implicit smooth prior injected by the triple-level augmentations, Aug-NeRF can
even recover scenes from heavily corrupted images, a highly challenging setting
untackled before. Our codes are available in
https://github.com/VITA-Group/Aug-NeRF.
