全局最优的内点集最大化同时摄像机位姿和特征对应
Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
摘要：
从单个图像相对于预先计算的3D点集估计相机的6-DoF姿态对于许多计算机视觉应用是重要的任务。视角n点（PSPs）求解器通常用于相机姿态估计，前提是预先知道一组高质量的2D-3D特征对应关系。然而，找到2D关键点和3D点集之间的最佳对应关系是不平凡的，特别是当仅知道几何（位置）信息时。现有的方法，同时构成和对应问题使用局部优化，因此不太可能找到最佳的解决方案，没有一个良好的姿势初始化，或引入限制性的假设。由于大部分离群值在这个问题中很常见，因此我们提出了一种全局最优的内点集基数最大化方法，该方法联合估计最佳相机姿态和最佳对应关系。我们的方法采用分支定界搜索的6D空间的相机构成，保证全局最优，而不需要一个姿势先验。SE（3）的几何结构用于找到内点数量的新的上界和下界，并集成了局部优化以加速收敛。评估经验支持的最优性证明，并表明，该方法比现有的方法，包括大规模的户外数据集上执行更强大。
英文摘要：
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.
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