Camera Uncertainty Computation in Large 3D Reconstruction

In computer vision, large scale Structure from Motion pipelines do not often evaluate the quality of the reconstruction by error propagation from measurements to the estimated parameters. It is a numerically sensitive and computationally challenging process, which is not easy to implement in practice for large scenes. We present a new algorithm that increases the numerical precision of the uncertainty propagation. It works with millions of feature points, thousands of cameras and millions of 3D points on a single computer. We provide an experimental comparison of our approach, as well as of previous approaches, on accurate ground truth and demonstrate that our algorithm is practical.