Key points
- NeRF represents a scene as a continuous volumetric function optimised from posed photographs.
- Differentiable volume rendering produces photorealistic novel views without explicit geometry.
- It is the named origin of Instant-NGP, Nerfstudio and 3D Gaussian splatting.
Summary
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.
Related items
- Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
- Nerfstudio: A Modular Framework for Neural Radiance Field Development
- 3D Gaussian Splatting for Real-Time Radiance Field Rendering
Source
Source: ECCV 2020 ↗ (Research)
Cite this item
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R. & Ng, R. (2020). ‘NeRF: Representing Scenes as Neural Radiance Fields’, ECCV 2020. Available at: https://arxiv.org/abs/2003.08934
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