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Combining Shape and Color Information For 3D Object Recognition

Both photometric and geometric information are important for 3D object recognition. Traditionally, however, few systems utilized both types of information. This is because no single representation is suitable for both types of information. This paper proposes a method for representing both color and geometric information using a common framework, the Spherical Attribute Image (SAI). The SAI maps the values of curvature and color computed at every node of a mesh approximating the object surface onto a spherical image. A model object and an observed surface are computed by finding the rotation that brings their spherical images into correspondence. We show how this matching algorithm can be used for object recognition using both geometric and photometric information. In addition, we describe how the two types of information can be combined in a way that takes into account their actual distribution on the surface.