论文标题
通过贪婪参数搜索优化消除模板
Optimizing Elimination Templates by Greedy Parameter Search
论文作者
论文摘要
我们提出了一种新方法，用于构建消除模板，以通过运动，图像匹配和摄像机跟踪来解决结构中最小问题的有效多项式系统。我们首先为具有有限数量不同解决方案的系统的消除模板构造了特定的仿射参数化。然后，我们在参数空间上使用启发式贪婪优化策略，以获取尺寸较小的模板。我们测试了计算机视觉中34个最小问题的方法。对于所有这些，我们发现了与最先进的模板相同或更小的模板。对于一些困难的例子，我们的模板是2.1、2.5、3.8、6.6倍的倍。对于焦距未知的折射绝对姿势估计问题，我们发现了一个小20倍的模板。我们对合成数据的实验还表明，新求解器在数值上是快速且数值准确的。我们还为相对姿势估计的问题提供了一个快速且数值准确的求解器，并具有未知的常见焦距和径向失真。
We propose a new method for constructing elimination templates for efficient polynomial system solving of minimal problems in structure from motion, image matching, and camera tracking. We first construct a particular affine parameterization of the elimination templates for systems with a finite number of distinct solutions. Then, we use a heuristic greedy optimization strategy over the space of parameters to get a template with a small size. We test our method on 34 minimal problems in computer vision. For all of them, we found the templates either of the same or smaller size compared to the state-of-the-art. For some difficult examples, our templates are, e.g., 2.1, 2.5, 3.8, 6.6 times smaller. For the problem of refractive absolute pose estimation with unknown focal length, we have found a template that is 20 times smaller. Our experiments on synthetic data also show that the new solvers are fast and numerically accurate. We also present a fast and numerically accurate solver for the problem of relative pose estimation with unknown common focal length and radial distortion.
