This letter is concerned with the problem of estimating camera orientation from a set of 2D/3D line correspondences, which is a major part of the Perspective-n-Line (PnL) problem. There are some cases that usually occur in real applications for PnL: the input line correspondences are corrupted by mismatches (a.k.a. outlier correspondences). The RANdom SAmple Consensus (RANSAC) algorithm is the de facto standard for solving outlier-contaminated PnL problems. However, RANSAC is a non-deterministic algorithm, which means that it produces a reasonable result only with a certain probability. Therefore, a PnL algorithm that could obtain a certifiably optimal solution from outlier-contaminated data is a matter of priority for some safety-critical applications. In this letter, we take a big step towards this goal by investigating globally optimal camera orientation estimation algorithms. Firstly, we decouple the rotation and translation estimation of a PnL problem by considering the geometrical property of the PnL problem. The Branch-and-Bound (BnB) algorithm is applied and it globally searches the entire rotation space to obtain the optimal camera orientation. To investigate the performance of our method, we tested the proposed algorithm on both synthetic and real data, and the results show that our algorithms can obtain the optimal camera orientation and are more robust than several state-of-the-art PnL methods.

中文翻译：

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通过 BnB 算法从线对应估计全局最优相机方向

这封信涉及从一组 2D/3D 线对应估计相机方向的问题，这是透视 n 线 (PnL) 问题的主要部分。在 PnL 的实际应用中通常会出现一些情况：输入行对应关系被不匹配（又名异常值对应）破坏。RANdom SAmple Consensus (RANSAC) 算法是解决异常值污染 PnL 问题的事实标准。然而，RANSAC 是一种非确定性算法，这意味着它只有在一定的概率下才会产生合理的结果。因此，可以从异常值污染的数据中获得可证明的最佳解决方案的 PnL 算法是某些安全关键应用程序的优先事项。在这封信中，通过研究全局最优相机方向估计算法，我们朝着这个目标迈出了一大步。首先，我们通过考虑 PnL 问题的几何特性来解耦 PnL 问题的旋转和平移估计。应用分支定界（BnB）算法，它全局搜索整个旋转空间以获得最佳相机方向。为了研究我们方法的性能，我们在合成数据和真实数据上测试了所提出的算法，结果表明我们的算法可以获得最佳的相机方向，并且比几种最先进的 PnL 方法更稳健。应用分支定界（BnB）算法，它全局搜索整个旋转空间以获得最佳相机方向。为了研究我们方法的性能，我们在合成数据和真实数据上测试了所提出的算法，结果表明我们的算法可以获得最佳的相机方向，并且比几种最先进的 PnL 方法更稳健。应用分支定界（BnB）算法，它全局搜索整个旋转空间以获得最佳相机方向。为了研究我们方法的性能，我们在合成数据和真实数据上测试了所提出的算法，结果表明我们的算法可以获得最佳的相机方向，并且比几种最先进的 PnL 方法更稳健。