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Not all range devices acquire, along with 3D data, the object's texture. Moreover, not always the desired texture is the visible light image. In such cases, currently, an “a posteriori” texturing of a 3D model is mostly performed in a manual or semi-automated fashion, resulting in a subjective and time consuming operation. Matching homologues points between 2D and 3D data in fact proved to be a more complex operation than image to image, or geometry to geometry registration. The method described in this paper is designed to be fully automated. The software takes as input a generic un-textured 3D model and a nonspecific texture image, which could be different from a visible light photograph, but belong to a set of diagnostic images like X rays, UV light, or IR images. It relies on the creation from the 3D model of several 2D depth maps which retains an exact correspondence with the points of the relief. Each depth map is generated from a different external “view point”. The number and location of such viewpoints is determined “a priori”, but their final position is to be changed and adjusted on a iterative and automatic base, to assure the possibility of an optimal choice. The selection of the best matching depth map is done by picking the depth map which shows the highest similarity with the texture image, based on a 2D-2D registration procedure performed on all generated depth maps. In order to speed up the procedure, a multi-resolution approach is adopted, where the coarse selection is performed on down-sampled images. Cross correlation and Maximization of Mutual Information (MMI) are here both used as similarity measures, exploiting their different and complementary performances depending on the image size.