RANSAC-based algorithms are the standard techniques for robust estimation in computer vision. These algorithms are iterative and computationally expensive; they alternate between random sampling of data, computing hypotheses, and running inlier counting. Many authors tried different approaches to improve efficiency. One of the major improvements is having a guided sampling, letting the RANSAC cycle stop sooner. This paper presents a new adaptive sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously computed scores in the sampling. In this paper, we derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. We test our method in multiple real-world datasets for several applications and obtain state-of-the-art results. Our method outperforms the baselines in accuracy while needing less computational time.

翻译：基于RANSAC的算法是计算机视觉中鲁棒估计的标准技术。这些算法具有迭代性和高计算成本的特点；它们在随机数据采样、假设计算以及内点计数之间交替进行。许多研究者尝试不同方法以提高效率。其中一项重要改进是采用引导采样，使RANSAC循环更早终止。本文提出了一种新的RANSAC自适应采样过程。先前的方法要么假设数据点内点/外点分类无先验信息，要么在采样中使用预先计算的得分。本文推导了一种动态贝叶斯网络，在RANSAC迭代过程中更新各个数据点的内点得分。每次迭代中，我们利用更新后的得分进行加权采样。我们的方法在有或无数据点先验得分的情况下均可工作。此外，我们利用更新的内点/外点得分推导出RANSAC循环的新停止准则。我们在多个真实世界数据集上针对不同应用测试了该方法，并取得了最优结果。与基线方法相比，我们的方法在保持更高准确率的同时，所需计算时间更少。