  Computational surface modeling that underlies material recognition has
transitioned from reflectance modeling using in-lab controlled radiometric
measurements to image-based representations based on internet-mined single-view
images captured in the scene. We take a middle-ground approach for material
recognition that takes advantage of both rich radiometric cues and flexible
image capture. A key concept is differential angular imaging, where small
angular variations in image capture enables angular-gradient features for an
enhanced appearance representation that improves recognition. We build a
large-scale material database, Ground Terrain in Outdoor Scenes (GTOS)
database, to support ground terrain recognition for applications such as
autonomous driving and robot navigation. The database consists of over 30,000
images covering 40 classes of outdoor ground terrain under varying weather and
lighting conditions. We develop a novel approach for material recognition
called texture-encoded angular network (TEAN) that combines deep encoding
pooling of RGB information and differential angular images for angular-gradient
features to fully leverage this large dataset. With this novel network
architecture, we extract characteristics of materials encoded in the angular
and spatial gradients of their appearance. Our results show that TEAN achieves
recognition performance that surpasses single view performance and standard
(non-differential/large-angle sampling) multiview performance.
