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.

采用微小角度变化捕获增加特征表达的差分角成像，在 GTOS 数据库上使用 TEAN 进行地面材料识别，其识别性能优于单视角和多视角性能。

地面材质识别的差分视角

Differential Viewpoints for Ground Terrain Material Recognition

We present a texture network called Deep Encoding Pooling Network (DEP) for
the task of ground terrain recognition. Recognition of ground terrain is an
important task in establishing robot or vehicular control parameters, as well
as for localization within an outdoor environment. The architecture of DEP
integrates orderless texture details and local spatial information and the
performance of DEP surpasses state-of-the-art methods for this task. The GTOS
database (comprised of over 30,000 images of 40 classes of ground terrain in
outdoor scenes) enables supervised recognition. For evaluation under realistic
conditions, we use test images that are not from the existing GTOS dataset, but
are instead from hand-held mobile phone videos of similar terrain. This new
evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground
terrain such as grass, gravel, asphalt and sand. The resultant network shows
excellent performance not only for GTOS-mobile, but also for more general
databases (MINC and DTD). Leveraging the discriminant features learned from
this network, we build a new texture manifold called DEP-manifold. We learn a
parametric distribution in feature space in a fully supervised manner, which
gives the distance relationship among classes and provides a means to
implicitly represent ambiguous class boundaries. The source code and database
are publicly available.

用 Deep Encoding Pooling Network (DEP) 进行地面地形识别，将无序纹理细节和局部空间信息相结合，超越了现有的先进方法，结果表明该网络在 GTOS-mobile 以及更一般的数据库上都表现出色。

深度纹理流形用于地面地形识别

Deep Texture Manifold for Ground Terrain Recognition

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.

该论文提出了一种基于微小角度变化的差分角成像技术的材料识别新方法，结合已有的丰富辐射特征和灵活的图像捕捉，在创建了一个大规模真实场景数据库 GTOS 的基础上，成功训练出一种叫做 DAIN 的网络，所得到的材料识别性能优于单视图或粗略量化的多视图图像识别方法。