Neural Radiance Field (NeRF)

Neural radiance fields (NeRFs)technique involves encoding the entire object or scene into an artificial neural network, which predicts the light intensity -- or radiance -- at any point in the 2D image to generate novel 3D views from different angles.

NeRFs show incredible promise in representing 3D data more efficiently than other techniques and could unlock new ways to generate highly realistic 3D objects automatically. Used with other techniques, NeRFs have incredible potential for massively compressing 3D representations of the world from gigabytes to tens of megabytes.

NeRFs can be used to generate 3D models of objects as well as for rendering 3D scenes for video games and for virtual and augmented reality environments. Google has already started using NeRFs to translate street map imagery into immersive views in Google Maps.

From Wikipedia

A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from sparse two-dimensional images. The NeRF model can learn the scene geometry, camera poses, and the reflectance properties of objects, allowing it to render photorealistic views of the scene from novel viewpoints. First introduced in 2020,[1] it has since gained significant attention for its potential applications in computer graphics and content creation.[2]

Algorithm[edit]

The NeRF encodes the scene as a volumetric function optimized by a fully connected deep neural network (DNN). NeRF can predict a volume density and view-dependent emitted radiance given the spatial location (x, y, z) and viewing direction in Euler angles (θ, Φ) of the camera. By sampling many points along camera rays, traditional volume rendering techniques can produce an image.[1]

https://en.wikipedia.org/wiki/Neural_radiance_field