Neural Radiance Field (NeRF)

Neural Radiance Field (NeRF) is a neural network that can reconstruct complex 3D scenes from a partial set of 2D images. A variety of simulations, games, media, and Internet of Things (IoT) applications require 3D images to make digital interactions more realistic and accurate. NeRF learns the scene geometry, objects, and angles of a specific scene, then renders realistic 3D views from new perspectives, automatically generating synthetic data to fill in the gaps.As a new field synthesis technology with implicit scene representation, it has attracted widespread attention in the field of computer vision.As a novel view synthesis and 3D reconstruction method, the NeRF model has been widely used in robotics, city mapping, autonomous navigation, virtual reality/augmented reality and other fields.

NeRF usage scenarios

NeRF can render complex scenes and generate images for a variety of use cases.

• Computer Graphics and Animation: In computer graphics, NeRF can be used to create realistic visual effects, simulations, and scenes. NeRF can capture, render, and project realistic environments, characters, and other images. NeRF is often used to improve video game graphics and VX movie animations.
• Medical Imaging: NeRF helps create comprehensive anatomy from 2D scans such as MRI. Their technology can reconstruct realistic representations of body tissues and organs, providing helpful visual context for doctors and medical technicians. 
• Virtual RealityNeRFs are an important technology in virtual reality and augmented reality simulations. Since they can accurately model 3D scenes, they help create and explore realistic virtual environments.
• Satellite imagery and planning: Satellite imagery provides a range of images that NeRF can use to generate a comprehensive model of the Earth's surface. It is very useful for reality capture (RC) use cases that require digitizing real-world environments. 

NeRF Architecture

NeRF uses a neural network building block of multi-layer perceptrons (MLPs), a fully connected neural network architecture, to create representations of 3D scenes. MLPs are a fundamental model in neural networks and deep learning. They are trained to map spatial coordinates and viewing directions to color and density values. MLPs use a series of mathematical structures to organize inputs (such as a position in 3D space or a 2D viewing direction) to determine the color and density values for each point in a 3D image.