  Recent work has demonstrated that deep learning approaches can successfully
be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of
a surface from as little as a single image. Closer inspection reveals, however,
that most approaches in the literature are trained purely on synthetic data,
which, while diverse and realistic, is often not representative of the richness
of the real world. In this paper, we show that training such networks
exclusively on synthetic data is insufficient to achieve adequate results when
tested on real data. Our analysis leverages a new dataset of real materials
obtained with a novel portable multi-light capture apparatus. Through an
extensive series of experiments and with the use of a novel deep learning
architecture, we explore two strategies for improving results on real data:
finetuning, and a per-material optimization procedure. We show that adapting
network weights to real data is of critical importance, resulting in an
approach which significantly outperforms previous methods for SVBRDF estimation
on real materials. Dataset and code are available at
https://lvsn.github.io/real-svbrdf
