  We present a means of formulating and solving the well known
structure-and-motion problem in computer vision with probabilistic graphical
models. We model the unknown camera poses and 3D feature coordinates as well as
the observed 2D projections as Gaussian random variables, using sigma point
parameterizations to effectively linearize the nonlinear relationships between
these variables. Those variables involved in every projection are grouped into
a cluster, and we connect the clusters in a cluster graph. Loopy belief
propagation is performed over this graph, in an iterative re-initialization and
estimation procedure, and we find that our approach shows promise in both
simulation and on real-world data. The PGM is easily extendable to include
additional parameters or constraints.
