Practical robust two-view translation estimation

Johan Fredriksson, Viktor Larsson, Carl Olsson
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引用次数: 26

Abstract

Outliers pose a problem in all real structure from motion systems. Due to the use of automatic matching methods one has to expect that a (sometimes very large) portion of the detected correspondences can be incorrect. In this paper we propose a method that estimates the relative translation between two cameras and simultaneously maximizes the number of inlier correspondences. Traditionally, outlier removal tasks have been addressed using RANSAC approaches. However, these are random in nature and offer no guarantees of finding a good solution. If the amount of mismatches is large, the approach becomes costly because of the need to evaluate a large number of random samples. In contrast, our approach is based on the branch and bound methodology which guarantees that an optimal solution will be found. While most optimal methods trade speed for optimality, the proposed algorithm has competitive running times on problem sizes well beyond what is common in practice. Experiments on both real and synthetic data show that the method outperforms state-of-the-art alternatives, including RANSAC, in terms of solution quality. In addition, the approach is shown to be faster than RANSAC in settings with a large amount of outliers.
实用的鲁棒双视图平移估计
在所有来自运动系统的真实结构中,异常值都是一个问题。由于使用自动匹配方法,人们不得不预料到检测到的对应关系中(有时是非常大的)一部分可能是不正确的。在本文中,我们提出了一种估计两个相机之间的相对平移并同时最大化早期对应数量的方法。传统上,使用RANSAC方法处理异常值去除任务。然而,这些本质上是随机的,并不能保证找到一个好的解决方案。如果不匹配的数量很大,由于需要评估大量的随机样本,该方法的成本会变得很高。相比之下,我们的方法是基于分支定界方法,保证找到最优解。虽然大多数最优方法以速度换取最优性,但所提出的算法在问题规模上具有竞争性的运行时间,远远超出了实践中常见的运行时间。在真实数据和合成数据上的实验表明,该方法在溶液质量方面优于最先进的替代方法,包括RANSAC。此外,在有大量异常值的情况下,该方法比RANSAC更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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