  Rendering 3D scenes requires access to arbitrary viewpoints from the scene.
Storage of such a 3D scene can be done in two ways; (1) storing 2D images taken
from the 3D scene that can reconstruct the scene back through interpolations,
or (2) storing a representation of the 3D scene itself that already encodes
views from all directions. So far, traditional 3D compression methods have
focused on the first type of storage and compressed the original 2D images with
image compression techniques. With this approach, the user first decodes the
stored 2D images and then renders the 3D scene. However, this separated
procedure is inefficient since a large amount of 2D images have to be stored.
In this work, we take a different approach and compress a functional
representation of 3D scenes. In particular, we introduce a method to compress
3D scenes by compressing the neural networks that represent the scenes as
neural radiance fields. Our method provides more efficient storage of 3D scenes
since it does not store 2D images -- which are redundant when we render the
scene from the neural functional representation.
