MAP model for large-scale 3D reconstruction and coarse matching for unordered wide-baseline photos

In this paper we presented a novel idea for large-scale 3D scene reconstruction and annealing based image grouping algorithm for unordered wide-baseline photos. Firstly, an alternative maximum a posterior (MAP) model which can easily incorporate image clustering prior knowledge is proposed. Second, an efficient annealing clustering algorithm is developed for organizing photos into clusters by calculating matching number of invariant features. Thirdly, we analyze the time complexity and efficiency of the proposed approach. Finally a series of experiments are performed on the real image data and synthetic data. The experimental result shows that the MAP model and relative annealing algorithm are efficient enough to tackle the large-scale 3D reconstruction problem, and it can be extended to solve other similar SFM parameters estimation problem as well.