Camera Uncertainty Computation in Large 3D Reconstruction
Michal Polic, Tomáš Pajdla · 2017
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.