  In this paper, we revisit the problem of local optimization in RANSAC. Once a
so-far-the-best model has been found, we refine it via Dual Principal Component
Pursuit (DPCP), a robust subspace learning method with strong theoretical
support and efficient algorithms. The proposed DPCP-RANSAC has far fewer
parameters than existing methods and is scalable. Experiments on estimating
two-view homographies, fundamental and essential matrices, and three-view
homographic tensors using large-scale datasets show that our approach
consistently has higher accuracy than state-of-the-art alternatives.
