  Neural Radiance Fields or NeRFs have become the representation of choice for
problems in view synthesis or image-based rendering, as well as in many other
applications across computer graphics and vision, and beyond. At their core,
NeRFs describe a new representation of 3D scenes or 3D geometry. Instead of
meshes, disparity maps, multiplane images or even voxel grids, they represent
the scene as a continuous volume, with volumetric parameters like
view-dependent radiance and volume density obtained by querying a neural
network. The NeRF representation has now been widely used, with thousands of
papers extending or building on it every year, multiple authors and websites
providing overviews and surveys, and numerous industrial applications and
startup companies. In this article, we briefly review the NeRF representation,
and describe the three decades-long quest to find the best 3D representation
for view synthesis and related problems, culminating in the NeRF papers. We
then describe new developments in terms of NeRF representations and make some
observations and insights regarding the future of 3D representations.
