  Texture, highlights, and shading are some of many visual cues that allow
humans to perceive material appearance in single pictures. Yet, recovering
spatially-varying bi-directional reflectance distribution functions (SVBRDFs)
from a single image based on such cues has challenged researchers in computer
graphics for decades. We tackle lightweight appearance capture by training a
deep neural network to automatically extract and make sense of these visual
cues. Once trained, our network is capable of recovering per-pixel normal,
diffuse albedo, specular albedo and specular roughness from a single picture of
a flat surface lit by a hand-held flash. We achieve this goal by introducing
several innovations on training data acquisition and network design. For
training, we leverage a large dataset of artist-created, procedural SVBRDFs
which we sample and render under multiple lighting directions. We further
amplify the data by material mixing to cover a wide diversity of shading
effects, which allows our network to work across many material classes.
Motivated by the observation that distant regions of a material sample often
offer complementary visual cues, we design a network that combines an
encoder-decoder convolutional track for local feature extraction with a
fully-connected track for global feature extraction and propagation. Many
important material effects are view-dependent, and as such ambiguous when
observed in a single image. We tackle this challenge by defining the loss as a
differentiable SVBRDF similarity metric that compares the renderings of the
predicted maps against renderings of the ground truth from several lighting and
viewing directions. Combined together, these novel ingredients bring clear
improvement over state of the art methods for single-shot capture of spatially
varying BRDFs.
