Texture is a fundamental characteristic of many types of images, and texture representation
is one of the essential and challenging problems in computer vision and pattern recognition …

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 …

Abstract Analysis-by-synthesis has been a successful approach for many tasks in computer
vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this …

We introduce a new light-field dataset of materials, and take advantage of the recent
success of deep learning to perform material recognition on the 4D light-field. Our dataset …

Self-supervised learning aims to learn image feature representations without the usage of
manually annotated labels. It is often used as a precursor step to obtain useful initial network …

Deep convolutional neural networks have recently proven extremely effective for difficult
face recognition problems in uncontrolled settings. To train such networks, very large …

The world is full of materials with interesting small-scale structure: a green pasture
consisting of millions of individual blades of grass, a scratched and partially rusted metallic …

Abstract The Bidirectional Texture Function (BTF) is a data‐driven solution to render
materials with complex appearance. A typical capture contains tens of thousands of images …

Collections of images under a single, uncontrolled illumination have enabled the rapid
advancement of core computer vision tasks like classification, detection, and segmentation …

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 …