This work concerns a novel study in the field of image-to-geometry registration. Our approach takes inspiration front medical imaging, in particular from multi-modal image registration. Most of the algorithms developed in this domain, where the images to register come from different sensors (CT, X-ray, PET), are based oil Mutual Information, a statistical measure of non-linear correlation between two data sources. The main idea is to use mutual information as a similarity measure between the image to be registered and renderings of the model geometry, in order to drive the registration in an iterative optimization framework. We demonstrate that some illumination-related geometric properties, such as surface normals, ambient occlusion and reflection directions call be used for this purpose. After a comprehensive analysis of such properties we propose a way to combine these sources of information in order to improve the performance of our automatic registration algorithm. The proposed approach call robustly cover a wide range of real cases and can be easily extended.
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