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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 takenfrom the 3D scene that can reconstruct the scene back through interpolations,or (2) storing a representation of the 3D scene itself that already encodesviews from all directions. So far, traditional 3D compression methods havefocused on the first type of storage and compressed the original 2D images withimage compression techniques. With this approach, the user first decodes thestored 2D images and then renders the 3D scene. However, this separatedprocedure is inefficient since a large amount of 2D images have to be stored.In this work, we take a different approach and compress a functionalrepresentation of 3D scenes. In particular, we introduce a method to compress3D scenes by compressing the neural networks that represent the scenes asneural radiance fields. Our method provides more efficient storage of 3D scenessince it does not store 2D images -- which are redundant when we render thescene from the neural functional representation.

Neural 3D Scene Compression via Model Compression