A neural radiance field (NeRF) is a neural network that reconstructs three-dimensional scenes from a set of two-dimensional images. By learning the geometry, lighting, and visual properties of a scene, it can generate photorealistic 3D views from angles that were never directly photographed.
The network learns how light behaves at every point in a scene, including color and how dense or transparent objects appear, so it can recreate the scene from any angle rather than just capturing a flat surface.
NeRFs work by combining traditional computer graphics techniques with deep learning. A multilayer perceptron (MLP) processes each point in 3D space to determine its color and density from any given viewpoint. Techniques like ray tracing, which simulates how light reflects and refracts through a scene, and volume rendering, which assigns color and density values to points in 3D space, are used together to produce the final image.
NeRFs are applied across computer graphics and animation, medical imaging, virtual and augmented reality, satellite imagery, and urban planning.