  We propose an approach based on convex relaxations for certifiably optimal
robust multiview triangulation. To this end, we extend existing relaxation
approaches to non-robust multiview triangulation by incorporating a truncated
least squares cost function. We propose two formulations, one based on epipolar
constraints and one based on fractional reprojection constraints. The first is
lower dimensional and remains tight under moderate noise and outlier levels,
while the second is higher dimensional and therefore slower but remains tight
even under extreme noise and outlier levels. We demonstrate through extensive
experiments that the proposed approaches allow us to compute provably optimal
reconstructions even under significant noise and a large percentage of
outliers.
