The increasing scale of Structure-from-Motion is fundamentally limited by the conventional optimization framework for the all-in-one global bundle adjustment. In this paper, we propose a distributed approach to coping with this global bundle adjustment for very large scale Structure-from-Motion computation. First, we derive the distributed formulation from the classical optimization algorithm ADMM, Alternating Direction Method of Multipliers, based on the global camera consensus. Then, we analyze the conditions under which the convergence of this distributed optimization would be guaranteed. In particular, we adopt over-relaxation and self-adaption schemes to improve the convergence rate. After that, we propose to split the large scale camera-point visibility graph in order to reduce the communication overheads of the distributed computing. The experiments on both public large scale SfM data-sets and our very large scale aerial photo sets demonstrate that the proposed distributed method clearly outperforms the state-of-the-art method in efficiency and accuracy.

中文翻译：

---

通过全球相机共识进行分布式超大规模捆绑调整。

从动结构的增长规模从根本上受到用于多合一全局捆包调整的常规优化框架的限制。在本文中，我们提出了一种分布式方法来应对这种大规模的捆扎调整，以进行大规模的动感结构计算。首先，我们基于全局相机共识从经典优化算法ADMM（乘数的交替方向方法）中得出分布式公式。然后，我们分析了可以保证此分布式优化收敛的条件。特别是，我们采用了过度松弛和自适应方案来提高收敛速度。此后，我们建议拆分大型摄像机点可见性图，以减少分布式计算的通信开销。