NeRF, or Neural Radiance Fields, is an emerging method in the field of computer vision and deep learning developed in 2020. Its primary role is in the generation and rendering of novel, high-quality 3D views of a scene from 2-Dimensional (2D) images. The fundamental concept of a NeRF is to use a fully connected (non-convolutional) neural network to model a continuous 3D scene function, with the network taking a 3D coordinate as an input and outputting the volume density and RGB color at that point.
NeRF models generate a 3D view by mapping every location in a scene to a color and transparency value through a fully connected deep network.It predicts the volume density and color of a volumetric scene representation, providing a way to create detailed 3D models from images taken from different viewpoints. The process involves “shooting” rays from a virtual camera into the 3D volume controlled by the network and sampling points along these rays to compute the rendered RGB pixel values.
This emerging technology offers vast possibilities for 3D imaging and has the potential to revolutionize industries such as film, gaming, VR/AR, and even real estate, by transforming simple 2D photos into detailed, high resolution, explorable 3D spaces. Despite its potential, NeRF still has some challenges to overcome, particularly in areas such as real-time rendering and dealing with dynamic or changing scenes. Subsequent research and innovations continue pushing the boundaries of this technology, showcasing its immense potential.