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.

中文翻译：

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用于束调整的多重网格

束调整是运动管线中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长的线性求解器的速度。对于大型问题，当前的技术水平与问题中的摄像机数量呈超线性关系。我们研究了全局束调整问题的条件，因为图像数量在不同的情况下增加，以及在当前最先进方法的超线性缩放方面的基本后果。我们提出了一个不平滑的聚合多重网格预处理器，它准确地表示了现有方法缩放不良的全局模式，并在大型、具有挑战性的问题集上展示了比现有技术快 13 倍的解决方案。