GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields

1Technical University of Munich, 2Meta Reality Labs Zurich

GANeRF takes advantage of generative adversarial networks (GAN) to improve quality of neural radiance fields (NeRF) in challenging real-world scenarios.


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.


GANeRF takes as input a set of posed images and optimizes for a 3D radiance field representation. Our core idea is to incorporate multi-view patch-based re-rendering constraints in an adversarial formulation that guides the NeRF reconstruction process, and to refine rendered images using a conditional generator network. Particularly in under-constrained regions this significantly improves the resulting rendering quality.



        title={GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields}, 
        author={Roessle, Barbara and M{\"u}ller, Norman and Porzi, Lorenzo and Bul{\`o}, Samuel Rota and Kontschieder, Peter and Nie{\ss}ner, Matthias},
        year = {2023},
        issue_date = {December 2023},
        publisher = {Association for Computing Machinery},
        address = {New York, NY, USA},
        volume = {42},
        number = {6},
        issn = {0730-0301},
        url = {},
        doi = {10.1145/3618402},
        journal = {ACM Trans. Graph.},
        month = {nov},
        articleno = {207},
        numpages = {14},