Barath Daniel, Noskova Jana, Matas Jiri
IEEE Trans Pattern Anal Mach Intell. 2022 Nov;44(11):8420-8432. doi: 10.1109/TPAMI.2021.3103562. Epub 2022 Oct 4.
A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac.
提出了一种用于鲁棒估计的新方法MAGSAC++。它引入了一种新的模型质量（评分）函数，该函数不做内点-外点决策，还提出了一种新颖的边缘化过程，该过程被公式化为一种M估计，使用一类新颖的M估计器（一种鲁棒核），通过迭代重加权最小二乘法求解。它不需要内点-外点阈值，只需要其宽松的上限，该上限可以从更宽的范围内选择。此外，我们提出了一种新的终止准则，以及一种在鲁棒估计完成后的后处理步骤中以数据驱动方式选择一组内点的技术。在多个用于单应性、基本矩阵拟合和相对姿态的公开真实世界数据集上，MAGSAC++产生了优于现有鲁棒方法的结果。它在几何上更精确，失败次数更少，而且通常速度更快。结果表明，与其他现有算法对内点-外点阈值的敏感性相比，MAGSAC++对阈值上限的设置敏感性显著更低。因此，它更容易应用于未见过的问题和场景，而无需手动获取关于内点-外点阈值设置的信息。C++和Python的源代码及示例可在https://github.com/danini/magsac获取。
