  We propose a robust and efficient method for multiview triangulation and
uncertainty estimation. Our contribution is threefold: First, we propose an
outlier rejection scheme using two-view RANSAC with the midpoint method. By
prescreening the two-view samples prior to triangulation, we achieve the
state-of-the-art efficiency. Second, we compare different local optimization
methods for refining the initial solution and the inlier set. With an iterative
update of the inlier set, we show that the optimization provides significant
improvement in accuracy and robustness. Third, we model the uncertainty of a
triangulated point as a function of three factors: the number of cameras, the
mean reprojection error and the maximum parallax angle. Learning this model
allows us to quickly interpolate the uncertainty at test time. We validate our
method through an extensive evaluation.
