论文标题
捆绑调整的跨部
Multigrid for Bundle Adjustment
论文作者
论文摘要
束调整是运动管道许多结构中重要的全局优化步骤。性能取决于用于计算最佳步骤的线性求解器的速度。对于大问题，当前艺术的状态与问题中的摄像机数量相比缩放。我们研究了全球束调整问题的调理，因为图像的数量在不同的制度和基本后果中增加了当前艺术方法的超线性缩放的基本后果。我们提出了一种不平滑的聚合综合预处理，它准确地代表了现有方法规模较差的全球模式，并证明在大型，具有挑战性的问题设置下的最高速度比最新的求解速度要快13倍。
Bundle adjustment is an important global optimization step in many structure from motion pipelines. Performance is dependent on the speed of the linear solver used to compute steps towards the optimum. For large problems, the current state of the art scales superlinearly with the number of cameras in the problem. We investigate the conditioning of global bundle adjustment problems as the number of images increases in different regimes and fundamental consequences in terms of superlinear scaling of the current state of the art methods. We present an unsmoothed aggregation multigrid preconditioner that accurately represents the global modes that underlie poor scaling of existing methods and demonstrate solves of up to 13 times faster than the state of the art on large, challenging problem sets.
