Comparison between asymptotic Bayesian approach and Kalman filter-based technique for 3D reconstruction using an image sequence

Two statistical approaches for 3-D reconstruction from an image sequence are compared: the asymptotic Bayesian surface reconstruction and the Kalman filter-based depth estimation. Both techniques are recursive algorithms where relevant information contained in previously taken images is summarized in a prior term (prior to the taking of the next image). This means that the reconstruction results are based upon information from all images but the storage and computation required do not grow dramatically. Experiments with both real images and computer generated images demonstrate that the asymptotic Bayesian approach achieves better results than the Kalman filter-based approach, largely due to better problem formulation