Title: Visual Perception of Materials and their Properties
Under typical viewing conditions, humans effortlessly recognize materials and infer their properties at a glance. Without touching materials, we can usually tell what they would feel like, and we enjoy vivid visual intuitions about how they are likely to respond if we interact with them. These achievements are impressive because the retinal image of a material results from extremely complex physical processes (e.g. sub-surface light transport; visco-elastic fluid flow). Due to their extreme diversity, mutability and complexity, materials represent a particularly challenging class of visual stimuli, so understanding how we recognize materials, estimate their properties, predict their behaviour, and interact with them could give us more general insights into visual processing. What is ‘material appearance’, and how do we measure it and model it? How are material properties estimated and represented? I will review work on the perception of optical properties (e.g., gloss, translucency) and mechanical properties (e.g., compliance, viscosity). Discussing these themes and questions causes us to scrutinize the basic assumptions of ‘inverse optics’ that prevail in theories of human vision, and gives hints at how to build a machine vision system that could learn materials from observation.