  Neural Radiance Fields (NeRF) have shown impressive novel view synthesis
results; nonetheless, even thorough recordings yield imperfections in
reconstructions, for instance due to poorly observed areas or minor lighting
changes. Our goal is to mitigate these imperfections from various sources with
a joint solution: we take advantage of the ability of generative adversarial
networks (GANs) to produce realistic images and use them to enhance realism in
3D scene reconstruction with NeRFs. To this end, we learn the patch
distribution of a scene using an adversarial discriminator, which provides
feedback to the radiance field reconstruction, thus improving realism in a
3D-consistent fashion. Thereby, rendering artifacts are repaired directly in
the underlying 3D representation by imposing multi-view path rendering
constraints. In addition, we condition a generator with multi-resolution NeRF
renderings which is adversarially trained to further improve rendering quality.
We demonstrate that our approach significantly improves rendering quality,
e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time
improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.
