Color photometric stereo and virtual image rendering using neural networks

In this paper we extend the application of neural network-based photometric stereo founded on the principle of empirical photometric stereo to color images proposing a method for computing both the normal vectors of a target object and its color reflectance coefficients. This method is able to render objects that have non-Lambert reflectance properties without using any parametric reflectance function as a reflectance model. In addition, we propose a novel neural network-based rendering method that allows the generation of realistic virtual images of an object with arbitrary light source direction and from arbitrary viewpoints based on the physical reflectance properties of the actual object and perform a comparative evaluation with approximations by existing models, the Phong model, and the Torrance–Sparrow model. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(12): 47–60, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20423