  We introduce a novel neural network-based BRDF model and a Bayesian framework
for object inverse rendering, i.e., joint estimation of reflectance and natural
illumination from a single image of an object of known geometry. The BRDF is
expressed with an invertible neural network, namely, normalizing flow, which
provides the expressive power of a high-dimensional representation,
computational simplicity of a compact analytical model, and physical
plausibility of a real-world BRDF. We extract the latent space of real-world
reflectance by conditioning this model, which directly results in a strong
reflectance prior. We refer to this model as the invertible neural BRDF model
(iBRDF). We also devise a deep illumination prior by leveraging the structural
bias of deep neural networks. By integrating this novel BRDF model and
reflectance and illumination priors in a MAP estimation formulation, we show
that this joint estimation can be computed efficiently with stochastic gradient
descent. We experimentally validate the accuracy of the invertible neural BRDF
model on a large number of measured data and demonstrate its use in object
inverse rendering on a number of synthetic and real images. The results show
new ways in which deep neural networks can help solve challenging radiometric
inverse problems.
