  In this paper, we introduce a technique to estimate measured BRDFs from a
sparse set of samples. Our approach offers accurate BRDF reconstructions that
are generalizable to new materials. This opens the door to BDRF reconstructions
from a variety of data sources. The success of our approach relies on the
ability of hypernetworks to generate a robust representation of BRDFs and a set
encoder that allows us to feed inputs of different sizes to the architecture.
The set encoder and the hypernetwork also enable the compression of densely
sampled BRDFs. We evaluate our technique both qualitatively and quantitatively
on the well-known MERL dataset of 100 isotropic materials. Our approach
accurately 1) estimates the BRDFs of unseen materials even for an extremely
sparse sampling, 2) compresses the measured BRDFs into very small embeddings,
e.g., 7D.
