  The so-called factorization methods recover 3-D rigid structure from motion
by factorizing an observation matrix that collects 2-D projections of features.
These methods became popular due to their robustness - they use a large number
of views, which constrains adequately the solution - and computational
simplicity - the large number of unknowns is computed through an SVD, avoiding
non-linear optimization. However, they require that all the entries of the
observation matrix are known. This is unlikely to happen in practice, due to
self-occlusion and limited field of view. Also, when processing long videos,
regions that become occluded often appear again later. Current factorization
methods process these as new regions, leading to less accurate estimates of 3-D
structure. In this paper, we propose a global factorization method that infers
complete 3-D models directly from the 2-D projections in the entire set of
available video frames. Our method decides whether a region that has become
visible is a region that was seen before, or a previously unseen region, in a
global way, i.e., by seeking the simplest rigid object that describes well the
entire set of observations. This global approach increases significantly the
accuracy of the estimates of the 3-D shape of the scene and the 3-D motion of
the camera. Experiments with artificial and real videos illustrate the good
performance of our method.
