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Neural Radiance Field (NeRF) regresses a neural parameterized scene bydifferentially rendering multi-view images with ground-truth supervision.However, when interpolating novel views, NeRF often yields inconsistent andvisually non-smooth geometric results, which we consider as a generalizationgap between seen and unseen views. Recent advances in convolutional neuralnetworks have demonstrated the promise of advanced robust data augmentations,either random or learned, in enhancing both in-distribution andout-of-distribution generalization. Inspired by that, we propose Augmented NeRF(Aug-NeRF), which for the first time brings the power of robust dataaugmentations into regularizing the NeRF training. Particularly, our proposallearns to seamlessly blend worst-case perturbations into three distinct levelsof the NeRF pipeline with physical grounds, including (1) the inputcoordinates, 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 themulti-view image supervision. Extensive results demonstrate that Aug-NeRFeffectively boosts NeRF performance in both novel view synthesis (up to 1.5dBPSNR gain) and underlying geometry reconstruction. Furthermore, thanks to theimplicit smooth prior injected by the triple-level augmentations, Aug-NeRF caneven recover scenes from heavily corrupted images, a highly challenging settinguntackled before. Our codes are available inhttps://github.com/VITA-Group/Aug-NeRF.

Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations