In this paper we propose a global optimization-based approach to jointly
matching a set of images. The estimated correspondences simultaneously maximize
pairwise feature affinities and cycle consistency across multiple images.
Unlike previous convex methods relying on semidefinite programming, we
formulate the problem as a low-rank matrix recovery problem and show that the
desired semidefiniteness of a solution can be spontaneously fulfilled. The
low-rank formulation enables us to derive a fast alternating minimization
algorithm in order to handle practical problems with thousands of features.
Both simulation and real experiments demonstrate that the proposed algorithm
can achieve a competitive performance with an order of magnitude speedup
compared to the state-of-the-art algorithm. In the end, we demonstrate the
applicability of the proposed method to match the images of different object
instances and as a result the potential to reconstruct category-specific object
models from those images.

本文提出一种全局优化的方法，用于同时匹配多张图像，将问题表述为低秩矩阵恢复问题并提供了一种快速交替最小化算法来解决该问题，在模拟和真实实验中，该算法与最先进的算法相比具有相同的计算性能但加速一个数量级。最后，证明了该方法的适用性，可以通过匹配不同对象实例的图像来重建特定类别的对象模型。