Bayesian estimation of 3D surfaces from a sequence of images

An approach is introduced to estimating object surfaces in 3D space from a sequence of images. A 3D surface of interest is modeled as a function known up to the values of a few parameters. Surface estimation is then treated as the general problem of maximum-likelihood parameter estimation based on two or more functionally related data sets, which constitute a sequence of images taken at different locations and orientations. Experiments are run to illustrate the various advantages of using as many images as possible in the estimation and of distributing camera positions from first to last over as large a baseline as possible. The authors introduce the use of asymptotic Bayesian approximations to summarize the useful information in a sequence of images, thereby drastically reducing both storage and processing. This results in a Bayesian estimator for the surface parameters. All the usual tools of statistical signal analysis can be brought to bear, the information extraction appears to be robust and computationally reasonable, the concepts are geometric and simple, and essentially optimal accuracy should result