3D object recognition based on hierarchical eigen-shapes and Bayesian inference

We present results of using Bayesian inference for recovering the 3-D shape and texture of an object based on information extracted from a single 2-D image. We are using a number of different models for specific object classes. The goal is to combine the classes to a hierarchical structure. Instead of searching for the most probable explanation we estimate the entire posterior distribution of the model parameters using Markov chain Monte Carlo methods. The evaluation of model fit is based on combining edge information with intensity difference between the model and the target image.