  Accurate estimation of camera matrices is an important step in structure from
motion algorithms. In this paper we introduce a novel rank constraint on
collections of fundamental matrices in multi-view settings. We show that in
general, with the selection of proper scale factors, a matrix formed by
stacking fundamental matrices between pairs of images has rank 6. Moreover,
this matrix forms the symmetric part of a rank 3 matrix whose factors relate
directly to the corresponding camera matrices. We use this new characterization
to produce better estimations of fundamental matrices by optimizing an L1-cost
function using Iterative Re-weighted Least Squares and Alternate Direction
Method of Multiplier. We further show that this procedure can improve the
recovery of camera locations, particularly in multi-view settings in which
fewer images are available.
