An iterative method for computing shape from shading using occluding boundary information is proposed.

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An object model for computer graphics applications should contain two aspects of information: shape and reflectance properties of the object. A number of techniques have been developed for modeling object shapes by observing real objects. In contrast, attempts to model reflectance properties of real objects have been rather limited. In most cases, modeled reflectance properties are too simple or too complicated to be used for synthesizing realistic images of the object.In this paper, we propose a new method for modeling object reflectance properties, as well as object shapes, by observing real objects. First, an object surface shape is reconstructed by merging multiple range images of the object. By using the reconstructed object shape and a sequence of color images of the object, parameters of a reflection model are estimated in a robust manner. The key point of the proposed method is that, first, the diffuse and specular reflection components are separated from the color image sequence, and then, reflectance parameters of each reflection component are estimated separately. This approach enables estimation of reflectance properties of real objects whose surfaces show specularity as well as diffusely reflected lights. The recovered object shape and reflectance properties are then used for synthesizing object images with realistic shading effects under arbitrary illumination conditions.

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Abstract

The presence of highlights, which in dielectric inhomogeneous objects are linear combination of specular and diffuse reflection components, is inevitable. A number of methods have been developed to separate these reflection components. To our knowledge, all methods that use a single input image require explicit color segmentation to deal with multicolored surfaces. Unfortunately, for complex textured images, current color segmentation algorithms are still problematic to segment correctly. Consequently, a method without explicit color segmentation becomes indispensable, and this paper presents such a method. The method is based solely on colors, particularly chromaticity, without requiring any geometrical parameter information. One of the basic ideas is to compare the intensity logarithmic differentiation of specular-free images and input images iteratively. The specular-free image is a pseudo-code of diffuse components that can be generated by shifting a pixel's intensity and chromaticity nonlinearly while retaining its hue. All processes in the method are done locally, involving a maximum of only two pixels. The experimental results on natural images show that the proposed method is accurate and robust under known scene illumination chromaticity. Unlike the existing methods that use a single image, our method is effective for textured objects with complex multicolored scenes.

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