Stochastic optimization of multiple texture registration using mutual information

We consider the problem of simultaneously registering several images to a 3D model. We propose a global approach based on mutual information that extends previous methods to incorporate the color, and does not require segmentation or feature extraction. We give a stochastic model for joint optimization of multiple image-to-model alignment and we propose a heuristic to solve it. Experiments with synthetic models showed that our algorithm is robust to varying illumination and surface characteristics. Experiments with real data showed that we can achieve very good accuracy even for an object with highly specular surface, in moderate lighting conditions.