title: GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
summary: 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.
paper_authors._id: 6486876813d16e2cf032960a
paper_authors.name: Barbara Roessle
paper_authors._id: 6486876813d16e2cf032960b
paper_authors.name: Norman Müller
paper_authors._id: 6486876813d16e2cf032960c
paper_authors.name: Lorenzo Porzi
paper_authors._id: 6486876813d16e2cf032960d
paper_authors.name: Samuel Rota Bulò
paper_authors._id: 6486876813d16e2cf032960e
paper_authors.name: Peter Kontschieder
paper_authors._id: 6486876813d16e2cf032960f
paper_authors.name: Matthias Nießner
paper_title: GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
paper_summary: 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.
paper_ai_keywords: Neural Radiance Fields (NeRF) novel view synthesis generative adversarial networks (GANs) adversarial discriminator 3D scene reconstruction patch-distribution rendering artifacts multi-view path rendering constraints multi-resolution NeRF adversarially trained LPIPS scores PSNR Tanks and Temples