| Literature DB >> 28697677 |
Abstract
Under typical viewing conditions, human observers effortlessly recognize materials and infer their physical, functional, and multisensory properties at a glance. Without touching materials, we can usually tell whether they would feel hard or soft, rough or smooth, wet or dry. We have vivid visual intuitions about how deformable materials like liquids or textiles respond to external forces and how surfaces like chrome, wax, or leather change appearance when formed into different shapes or viewed under different lighting. These achievements are impressive because the retinal image results from complex optical interactions between lighting, shape, and material, which cannot easily be disentangled. Here I argue that because of the diversity, mutability, and complexity of materials, they pose enormous challenges to vision science: What is material appearance, and how do we measure it? How are material properties estimated and represented? Resolving these questions causes us to scrutinize the basic assumptions of mid-level vision.Entities:
Keywords: deep learning; mid-level vision; reflectance; surfaces; texture; transparency
Mesh:
Year: 2017 PMID: 28697677 DOI: 10.1146/annurev-vision-102016-061429
Source DB: PubMed Journal: Annu Rev Vis Sci ISSN: 2374-4642 Impact factor: 6.422