A curated list of awesome neural radiance fields papers, inspired by awesome-computer-vision.
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, Mildenhall et al., ECCV 2020 | github | bibtex
- NeRF++: Analyzing and Improving Neural Radiance Fields, Zhang et al., Arxiv 2020 | github | bibtex
- DeRF: Decomposed Radiance Fields, Rebain et al. Arxiv 2020 | bibtex
- Neural Sparse Voxel Fields, Liu et al., NeurIPS 2020 | github | bibtex
- AutoInt: Automatic Integration for Fast Neural Volume Rendering, Lindell et al., Arxiv 2020 | bibtex
- GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis, Schwarz et al., NeurIPS 2020 | github | bibtex
- NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections, Martin-Brualla et al., Arxiv 2020 | bibtex
- Deformable Neural Radiance Fields, Park et al., Arxiv 2020 | bibtex
- D-NeRF: Neural Radiance Fields for Dynamic Scenes, Pumarola et al., Arxiv 2020 | bibtex
- Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes, Li et al., Arxiv 2020 | bibtex
- Space-time Neural Irradiance Fields for Free-Viewpoint Video, Xian et al., Arxiv 2020 | bibtex
- GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering, Trevithick and Yang, Arxiv 2020 | github | bibtex
- pixelNeRF: Neural Radiance Fields from One or Few Images, Yu et al., Arxiv 2020 | github | bibtex
- Learned Initializations for Optimizing Coordinate-Based Neural Representations, Tancik et al., Arxiv 2020 | bibtex
- pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis, Chan et al., Arxiv 2020 | bibtex
- NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, Ben Mildenhall
- Understanding and Extending Neural Radiance Fields, Barron et al.
- Towards Photorealism (2nd half), Vladlen Koltun
- Neural Radiance Fields for View Synthesis, Matthew Tancik
MIT