IEEE Trans Cybern. 2020 Oct;50(10):4530-4543. doi: 10.1109/TCYB.2018.2889908. Epub 2019 Jan 11.
The performance of many robust model fitting techniques is largely dependent on the quality of the generated hypotheses. In this paper, we propose a novel guided sampling method, called accelerated guided sampling (AGS), to efficiently generate the accurate hypotheses for multistructure model fitting. Based on the observations that residual sorting can effectively reveal the data relationship (i.e., determine whether two data points belong to the same structure), and keypoint matching scores can be used to distinguish inliers from gross outliers, AGS effectively combines the benefits of residual sorting and keypoint matching scores to efficiently generate accurate hypotheses via information theoretic principles. Moreover, we reduce the computational cost of residual sorting in AGS by designing a new residual sorting strategy, which only sorts the top-ranked residuals of input data, rather than all input data. Experimental results demonstrate the effectiveness of the proposed method in computer vision tasks, such as homography matrix and fundamental matrix estimation.
许多强大的模型拟合技术的性能在很大程度上取决于所生成假设的质量。在本文中，我们提出了一种新的引导采样方法，称为加速引导采样（AGS），可有效地为多结构模型拟合生成准确的假设。基于残差排序可以有效地揭示数据关系（即确定两个数据点是否属于同一结构）的观察结果，以及关键点匹配分数可用于区分内点和粗外点的观察结果，AGS 有效地结合了残差排序和关键点匹配分数的优势，通过信息论原理有效地生成准确的假设。此外，我们通过设计一种新的残差排序策略来降低 AGS 中的计算成本，该策略仅对输入数据的前几个残差进行排序，而不是对所有输入数据进行排序。实验结果表明，该方法在计算机视觉任务（如单应矩阵和基础矩阵估计）中是有效的。
