Estimating the absolute camera pose requires 3D-to-2D correspondences of points and/or lines. However, in practice, these correspondences are inevitably corrupted by outliers, which affects the pose estimation. Existing outlier removal strategies for robust pose estimation have some limitations. They are only applicable to points, rely on prior pose information, or fail to handle high outlier ratios. By contrast, we propose a general and accurate outlier removal strategy. It can be integrated with various existing pose estimation methods originally vulnerable to outliers, and is applicable to points, lines, and the combination of both. Moreover, it does not rely on any prior pose information. Our strategy has a nested structure composed of the outer and inner modules. First, our outer module leverages our intersection constraint , i.e., the projection rays or planes defined by inliers intersect at the camera center. Our outer module alternately computes the inlier probabilities of correspondences and estimates the camera pose. It can run reliably and efficiently under high outlier ratios. Second, our inner module exploits our flow consensus . The 2D displacement vectors or 3D directed arcs generated by inliers exhibit a common directional regularity, i.e., follow a dominant trend of flow. Our inner module refines the inlier probabilities obtained at each iteration of our outer module. This refinement improves the accuracy and facilitates the convergence of our outer module. Experiments on both synthetic data and real-world images have shown that our method outperforms state-of-the-art approaches in terms of accuracy and robustness.

中文翻译：

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通过交叉点约束和流一致性对绝对相机位姿进行鲁棒估计。


估计绝对相机位姿需要点和/或线的 3D 到 2D 对应关系。然而，在实践中，这些对应关系不可避免地会被异常值破坏，从而影响姿态估计。现有的稳健姿态估计的异常值去除策略存在一些局限性。它们仅适用于点，依赖于先验姿势信息，或无法处理高异常值比率。相比之下，我们提出了一种通用且准确的异常值去除策略。它可以与现有的各种原本容易受到异常值影响的位姿估计方法集成，并且适用于点、线以及两者的组合。此外，它不依赖于任何先前的姿势信息。我们的策略具有由外部模块和内部模块组成的嵌套结构。首先，我们的外部模块利用我们的相交约束，即由内点定义的投影光线或平面在相机中心相交。我们的外部模块交替计算对应的内部概率并估计相机姿态。它可以在高异常值率下可靠、高效地运行。其次，我们的内部模块利用了我们的流量共识。由内点生成的 2D 位移矢量或 3D 定向弧表现出共同的方向规律性，即遵循流动的主导趋势。我们的内部模块改进了外部模块每次迭代时获得的内部概率。这种细化提高了准确性并促进了外部模块的收敛。对合成数据和真实图像的实验表明，我们的方法在准确性和鲁棒性方面优于最先进的方法。