@article{nerfneuralradiancefields2020,
  title = {NeRF: Representing Scenes as Neural Radiance Fields},
  author = {Mildenhall, B. and Srinivasan, P. P. and Tancik, M. and Barron, J. T. and Ramamoorthi, R. and Ng, R.},
  year = {2020},
  journal = {ECCV 2020},
  url = {https://arxiv.org/abs/2003.08934},
  abstract = {Mildenhall et al. (2020) introduced Neural Radiance Fields, a method that represents a 3D scene as a continuous function mapping spatial coordinates and viewing direction to colour and volume density, parameterised by a multilayer perceptron optimised from a set of posed input photographs. Differentiable volume rendering integrates the field along camera rays to produce novel-view images that match the input photographs. NeRF received an ECCV 2020 Honourable Mention and spawned a large successor family including Instant-NGP (real-time training), Nerfstudio (production tooling) and 3D Gaussian Splatting (explicit primitive representation). These successors are relevant to VFX and virtual-production workflows; none of them are self-explanatory without NeRF.},
  keywords = {3d-generation, vfx-production},
  note = {AI \& Animation Education Knowledge Base}
}