  Global methods to Structure from Motion have gained popularity in recent
years. A significant drawback of global methods is their sensitivity to
collinear camera settings. In this paper, we introduce an analysis and
algorithms for averaging bifocal tensors (essential or fundamental matrices)
when either subsets or all of the camera centers are collinear.
  We provide a complete spectral characterization of bifocal tensors in
collinear scenarios and further propose two averaging algorithms. The first
algorithm uses rank constrained minimization to recover camera matrices in
fully collinear settings. The second algorithm enriches the set of possibly
mixed collinear and non-collinear cameras with additional, "virtual cameras,"
which are placed in general position, enabling the application of existing
averaging methods to the enriched set of bifocal tensors. Our algorithms are
shown to achieve state of the art results on various benchmarks that include
autonomous car datasets and unordered image collections in both calibrated and
unclibrated settings.
