Most robust estimators require tuning the parameters of the algorithm for the particular application, a bottleneck for practical applications. The paper presents the multiple input structures with robust estimator (MISRE), where each structure, inlier or outlier, is processed independently. The same two constants are used to find the scale estimates over expansions for each structure. The inlier/outlier classification is straightforward since the data is processed and ordered with the relevant inlier structures listed first. If the inlier noises are similar, MISRE’s performance is equivalent to RANSAC-type algorithms. MISRE still returns the correct inlier estimates when inlier noises are very different, while RANSAC-type algorithms do not perform as well. MISRE’s failures are gradual when too many outliers are present, beginning with the least significant inlier structure. Examples from 2D images and 3D point clouds illustrate the estimation.

中文翻译：

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参数结构鲁棒估计的新方法


大多数稳健的估计器需要针对特定​​应用调整算法参数，这是实际应用的瓶颈。该论文提出了具有鲁棒估计器（MISRE）的多输入结构，其中每个结构（无论是内点还是离群点）都是独立处理的。相同的两个常数用于查找每个结构的扩展规模估计。内部值/异常值分类很简单，因为数据是按照首先列出的相关内部值结构进行处理和排序的。如果内点噪声相似，MISRE 的性能与 RANSAC 类型算法相当。当内点噪声非常不同时，MISRE 仍然返回正确的内点估计，而 RANSAC 类型算法的性能则不佳。当存在太多异常值时，MISRE 的失败是渐进的，从最不重要的内部结构开始。 2D 图像和 3D 点云的示例说明了该估计。