What is a Neural Render Field (NeRF)?

A Neural Render Field or NeRF is a neural network that reconstructs complex three-dimensional scenes and objects via partial two-dimensional images. These NeRF generations calculate and estimate geometry, angles and lighting values filling in the gaps using neural calculations.

Geometric aspects and complex shapes, angles, and positional data are calculated within the 3D scene alongside lighting, reflections, and transparency values, which are determined by interpreting the data sets to each other. These neural generations can calculate illumination and textural values allowing for relighting these scenes with materials such as metal and glass being estimated.

NeRFs come in a variety of forms using different algorithms to calculate values from these camera rays: PixelNeRF, NSVF, Pelnoptic Voxel and Mega-NeRD to name a few. NeRFs operate in a similar vein to Raytracing with 2D photos overlapping each other and depth layers separated to generate the depth.

Neural Render Fields are more optimal than Photogrammetry methods BUT are built off similar principles with the downside being the end result being harder to work with if needing to be converted to a workable editable mesh.