A new approach is introduced to estimating object surfaces in three-dimensional space from a sequence of images. A surface of interest is modeled as a 3-D function known up to the values of a few parameters. The authors have modeled objects as patches of spheres, cylinders, and planes--primitive objects. These primitive surfaces are special cases of 3-D quadric surfaces. Primitive surface estimation is treated as the general problem of maximum likelihood parameter estimation based on two or more functionally related data sets. A simple geometric explanation is given for the estimation algorithm. They introduce the use of asymptotic Bayesian approximations in order to summarize the useful information in a sequence of images, thereby drastically reducing both the storage and amount of processing required. The attractiveness of the Bayesian approach is that now 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.
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