Frequency-based heuristics for material perception.

People often make rapid visual judgments of the properties of surfaces they are going to walk on or touch. How do they do this when the interactions of illumination geometry with 3-D material structure and object shape result in images that inverse optics algorithms cannot resolve without externally imposed constraints? A possibly effective strategy would be to use heuristics based on information that can be gleaned rapidly from retinal images. By using perceptual scaling of a large sample of images, combined with correspondence and canonical correlation analyses, we discovered that material properties, such as roughness, thickness, and undulations, are characterized by specific scales of luminance variations. Using movies, we demonstrate that observers' percepts of these 3-D qualities vary continuously as a function of the relative energy in corresponding 2-D frequency bands. In addition, we show that judgments of roughness, thickness, and undulations are predictably altered by adaptation to dynamic noise at the corresponding scales. These results establish that the scale of local 3-D structure is critical in perceiving material properties, and that relative contrast at particular spatial frequencies is important for perceiving the critical 3-D structure from shading cues, so that cortical mechanisms for estimating material properties could be constructed by combining the parallel outputs of sets of frequency-selective neurons. These results also provide methods for remote sensing of material properties in machine vision, and rapid synthesis, editing and transfer of material properties for computer graphics and animation.