Issue No. 12 - Dec. (2013 vol. 35)
David J. Crandall , Dept. of Comput. Sci., Cornell Univ., Ithaca, NY, USA
Andrew Owens , Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
Noah Snavely , Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, USA
Daniel P. Huttenlocher , Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, USA
Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM based on finding a coarse initial solution using hybrid discrete-continuous optimization and then improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random field (MRF) formulation, coupled with a continuous Levenberg-Marquardt refinement. The formulation naturally incorporates various sources of information about both the cameras and points, including noisy geotags and vanishing point (VP) estimates. We test our method on several large-scale photo collections, including one with measured camera positions, and show that it produces models that are similar to or better than those produced by incremental bundle adjustment, but more robustly and in a fraction of the time.
Cameras, Optimization, Robustness, Image reconstruction, Noise measurement, Belief propagation, Motion analysis,belief propagation, Structure from motion, 3D reconstruction, Markov random fields
David J. Crandall, Andrew Owens, Noah Snavely, Daniel P. Huttenlocher, "SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 2841-2853, Dec. 2013, doi:10.1109/TPAMI.2012.218