ANSAC：自适应非最小样本和一致性
ANSAC: Adaptive Non-minimal Sample and Consensus
摘要：
虽然基于RANSAC的方法对不正确的图像对应（离群值）具有鲁棒性，但它们的假设生成器对具有位置误差（噪声）的正确图像对应（内点）不具有鲁棒性。这会减慢它们的收敛速度，因为从最小的噪声内点集合中提取的假设可能会显著偏离最优模型。这项工作通过引入ANSAC来解决这个问题，ANSAC是一种基于RANSAC的估计器，它通过自适应地使用多于生成假设所需的最小数量的对应关系来考虑噪声。ANSAC估计候选对应的几个排序子集的内点比率（正确对应的分数），并从中生成假设。它的假设生成机制优先使用具有高内围值比率的子集来生成高质量的假设。ANSAC使用提前终止标准，该标准跟踪内围值比率历史，并在一段时间内未发生显著变化时终止。实验表明，ANSAC在几次迭代中找到了良好的单应性和基本矩阵估计，始终优于最先进的方法。
英文摘要：
While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise). This slows down their convergence because hypotheses drawn from a minimal set of noisy inliers can deviate significantly from the optimal model. This work addresses this problem by introducing ANSAC, a RANSAC-based estimator that accounts for noise by adaptively using more than the minimal number of correspondences required to generate a hypothesis. ANSAC estimates the inlier ratio (the fraction of correct correspondences) of several ranked subsets of candidate correspondences and generates hypotheses from them. Its hypothesis-generation mechanism prioritizes the use of subsets with high inlier ratio to generate high-quality hypotheses. ANSAC uses an early termination criterion that keeps track of the inlier ratio history and terminates when it has not changed significantly for a period of time. The experiments show that ANSAC finds good homography and fundamental matrix estimates in a few iterations, consistently outperforming state-of-the-art methods.
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