论文标题
学习优化非刚性跟踪
Learning to Optimize Non-Rigid Tracking
论文作者
论文摘要
非刚性跟踪的广泛解决方案之一具有嵌套环结构：使用高斯 - 纽顿（Gauss-Newton）最大程度地减少外环中的跟踪目标，并使用预处理的共轭梯度（PCG）求解内环中的稀疏线性系统。在本文中，我们采用可学习的优化来改善跟踪鲁棒性并加快求解器的融合。首先，我们通过在通过CNN端到端学习的深度功能上集成对齐数据术语来升级跟踪目标。新的跟踪目标可以捕获全球变形，这有助于高斯·纽顿（Gauss-Newton）超越本地最小值，从而导致对大型非刚性运动的强大跟踪。其次，我们通过引入一个经过训练以生成预处理的条件网络来弥合预处理技术和学习方法之间的差距，以便PCG可以在少数步骤中收敛。实验结果表明，所提出的学习方法比原始PCG的收敛速度更快。
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop. In this paper, we employ learnable optimizations to improve tracking robustness and speed up solver convergence. First, we upgrade the tracking objective by integrating an alignment data term on deep features which are learned end-to-end through CNN. The new tracking objective can capture the global deformation which helps Gauss-Newton to jump over local minimum, leading to robust tracking on large non-rigid motions. Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner such that PCG can converge within a small number of steps. Experimental results indicate that the proposed learning method converges faster than the original PCG by a large margin.
