  Material recognition for real-world outdoor surfaces has become increasingly
important for computer vision to support its operation "in the wild."
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 images of
materials captured in the scene. We propose to take a middle-ground approach
for material recognition that takes advantage of both rich radiometric cues and
flexible image capture. We realize this by developing a framework for
differential angular imaging, where small angular variations in image capture
provide an enhanced appearance representation and significant recognition
improvement. We build a large-scale material database, Ground Terrain in
Outdoor Scenes (GTOS) database, geared towards real use for autonomous agents.
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 a Differential Angular Imaging
Network (DAIN) 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 DAIN achieves
recognition performance that surpasses single view or coarsely quantized
multiview images. These results demonstrate the effectiveness of differential
angular imaging as a means for flexible, in-place material recognition.
