  Bidirectional reflectance distribution functions (BRDFs) are pervasively used
in computer graphics to produce realistic physically-based appearance. In
recent years, several works explored using neural networks to represent BRDFs,
taking advantage of neural networks' high compression rate and their ability to
fit highly complex functions. However, once represented, the BRDFs will be
fixed and therefore lack flexibility to take part in follow-up operations. In
this paper, we present a form of "Neural BRDF algebra", and focus on both
representation and operations of BRDFs at the same time. We propose a
representation neural network to compress BRDFs into latent vectors, which is
able to represent BRDFs accurately. We further propose several operations that
can be applied solely in the latent space, such as layering and interpolation.
Spatial variation is straightforward to achieve by using textures of latent
vectors. Furthermore, our representation can be efficiently evaluated and
sampled, providing a competitive solution to more expensive Monte Carlo
layering approaches.
