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Guaranteed Outlier Removal for Point Cloud Registration with Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2017-11-14 , DOI: 10.1109/tpami.2017.2773482
Alvaro Parra Bustos , Tat-Jun Chin

An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust techniques to require very high computational cost. In this paper, we present a novel preprocessing method called guaranteed outlier removal for point cloud registration. Our method reduces the input to a smaller set, in a way that any rejected correspondence is guaranteed to not exist in the globally optimal solution. The reduction is performed using purely geometric operations which are deterministic and fast. Our method significantly reduces the population of outliers, such that further optimization can be performed quickly. Further, since only true outliers are removed, the globally optimal solution is preserved. On various synthetic and real data experiments, we demonstrate the effectiveness of our preprocessing method. Demo code is available as supplementary material , which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2773482 .

中文翻译:

保证对应点云注册的异常值去除

3D点云注册的一种既定方法是根据3D关键点对应关系估算注册功能。通常,由于存在错误的对应关系或离群值,因此需要鲁棒的技术来进行估计。当前的3D关键点技术比2D关键点技术精确度低得多,因此它们往往会产生极高的异常值。因此必须提取大量的推定对应关系,以确保有足够的良好对应关系可用。但是,这两个因素(高异常率,大数据量)都会导致现有的鲁棒技术需要很高的计算成本。在本文中,我们提出了一种新颖的预处理方法,称为 保证消除异常值 点云注册。我们的方法将输入减少到较小的集合,以确保在全局最优解中不存在任何被拒绝的对应关系。减少是使用确定性和快速性的纯几何运算执行的。我们的方法大大减少了异常值的数量,因此可以快速执行进一步的优化。此外,由于仅去除了真实的异常值,因此保留了全局最优解。在各种合成和真实数据实验中,我们证明了预处理方法的有效性。 演示代码可作为补充材料 ,可以在以下网站的计算机协会数字图书馆中找到: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2773482
更新日期:2018-11-05
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