  Images are the standard input for vision algorithms, but one-shot infield
reflectance measurements are creating new opportunities for recognition and
scene understanding. In this work, we address the question of what reflectance
can reveal about materials in an efficient manner. We go beyond the question of
recognition and labeling and ask the question: What intrinsic physical
properties of the surface can be estimated using reflectance? We introduce a
framework that enables prediction of actual friction values for surfaces using
one-shot reflectance measurements. This work is a first of its kind
vision-based friction estimation. We develop a novel representation for
reflectance disks that capture partial BRDF measurements instantaneously. Our
method of deep reflectance codes combines CNN features and fisher vector
pooling with optimal binary embedding to create codes that have sufficient
discriminatory power and have important properties of illumination and spatial
invariance. The experimental results demonstrate that reflectance can play a
new role in deciphering the underlying physical properties of real-world
scenes.
