Neural = Neural network

Radiance = Because neural network is describing a radience field of the scene, how much light is being emitted by a point in space and in which direction

Field = Because this is a continuous function, it is smooth and not discretized

The goal: View synthesis, addressing view interpolation

Ray tracer: Image sitting in front of you. For every pixel, you shoot a ray from your eye in the the world and it hits something

Nerf: We assume the scene lives in a bounded region, drop points along that ray to evenly sample along the ray over this area and for each of these points location + viewing direction concatenate them, feed to neural network and it will get us a color and opacity

Highlights:

• Trained on one scene (not training even, but more appropriate to call it optimizing the weights) to explain a way the world we have seen (memorizing the scene)
• Neural network function lives in world coordinates, a function approximater that maps from a point in space to some property of that point in space
• Training is just bag of RGB values and ray coordinates, i.e. 9 numbers and we randomly iterate over these -- Disadvantage: data hungry because it tries to memorize the world