Abstract

We present the design and implementation of a new inex- act Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Con- jugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient precondition- ers. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjust- ment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available