论文标题
更明智的随机样本共识
More Informed Random Sample Consensus
论文作者
论文摘要
随机样品共识（RANSAC）是一种可靠的模型拟合算法。它被广泛用于许多领域，包括图像粘结和点云注册。在RANSAC中，对假设产生的数据均匀采样。但是，这种统一的抽样策略并未完全利用许多问题。在本文中，我们提出了一种用Lévy分布的数据以及数据排序算法的方法。在所提出方法的假设采样步骤中，使用我们提出的分类算法对数据进行排序，该算法基于数据点在Inlier集合中的可能性进行分类。然后，从具有Lévy分布的分类数据中对假设进行采样。在模拟和现实世界公共数据集上评估了所提出的方法。与统一基线方法相比，我们的方法显示出更好的结果。
Random sample consensus (RANSAC) is a robust model-fitting algorithm. It is widely used in many fields including image-stitching and point cloud registration. In RANSAC, data is uniformly sampled for hypothesis generation. However, this uniform sampling strategy does not fully utilize all the information on many problems. In this paper, we propose a method that samples data with a Lévy distribution together with a data sorting algorithm. In the hypothesis sampling step of the proposed method, data is sorted with a sorting algorithm we proposed, which sorts data based on the likelihood of a data point being in the inlier set. Then, hypotheses are sampled from the sorted data with Lévy distribution. The proposed method is evaluated on both simulation and real-world public datasets. Our method shows better results compared with the uniform baseline method.
