A method called, sigma-consensus, is proposed to eliminate the need for a
user-defined inlier-outlier threshold in RANSAC. Instead of estimating the
noise sigma, it is marginalized over a range of noise scales. The optimized
model is obtained by weighted least-squares fitting where the weights come from
the marginalization over sigma of the point likelihoods of being inliers. A new
quality function is proposed not requiring sigma and, thus, a set of inliers to
determine the model quality. Also, a new termination criterion for RANSAC is
built on the proposed marginalization approach. Applying sigma-consensus,
MAGSAC is proposed with no need for a user-defined sigma and improving the
accuracy of robust estimation significantly. It is superior to the
state-of-the-art in terms of geometric accuracy on publicly available
real-world datasets for epipolar geometry (F and E) and homography estimation.
In addition, applying sigma-consensus only once as a post-processing step to
the RANSAC output always improved the model quality on a wide range of vision
problems without noticeable deterioration in processing time, adding a few
milliseconds. The source code is at this https URL

该论文提出了一种称为 sigma-consensus 的方法，用于消除 RANSAC 中需要一个用户定义的内点外点阈值的需要。该方法通过边缘化一系列噪声比例来估计噪声 sigma，并基于所有可能是内点的点的似然性 sigma 的边缘化进行加权最小二乘拟合来优化模型。使用 sigma-consensus，提出了 MAGSAC 方法，无需用户定义 sigma，并显著提高了鲁棒估计的几何精度，优于公开可用的实际数据集上的最新技术，如基线和单应性估计。此外，将 sigma-consensus 仅作为后处理步骤应用于 RANSAC 输出时，始终可以提高广泛的视觉问题的模型质量，几乎不会影响处理时间。