Texture, illumination, and material perception

Abstract

In this paper we will present an overview of our research into perception and biologically inspired modeling of illumination (flow) from 3D textures and the influence of roughness and illumination on material perception. Here 3D texture is defined as an image of an illuminated rough surface. In a series of theoretical and empirical papers we studied how we can estimate the illumination orientation (in the image plane) from 3D textures of globally flat samples. We found that the orientation can be estimated well by humans and computers using an approach based on second order statistics. This approach makes use of the dipole-like structures in 3D textures that are the results of illumination of bumps / throughs. For 3D objects, the local illumination direction varies over the object, resulting in surface illuminance flow. This again results in image illuminance flow in the image of a rough 3D object: the observable projection in the image of the field of local illumination orientations. Here we present results on image illuminance flow analysis for images from the Utrecht Oranges database, the Curet database and two vases. These results show that the image illuminance flow can be estimated robustly for various rough materials. In earlier studies we have shown that the image illuminance flow can be used to do shape and illumination inferences. Recently, in psychophysical experiments we found that adding 3D texture to a matte spherical object improves judgments of the direction and diffuseness of its illumination by human observers. This shows that human observers indeed use the illuminance flow as a cue for the illumination.