基于内点尺度估计和子集更新的迭代MaxFS多结构确定性拟合
Deterministic Fitting of Multiple Structures using Iterative MaxFS with Inlier Scale Estimation and Subset Updating
摘要：
基于最大可行子系统（MaxFS）框架，提出了一种高效的多结构鲁棒拟合的确定性假设生成算法。尽管具有优势，但全局优化方法（如MaxFS）对于几何模型拟合具有两个主要限制。首先，它的性能很大程度上受用户指定的内点尺度的影响。其次，它对于大数据计算效率低。所提出的基于MaxFS的算法迭代地估计模型参数和内点规模，并通过减少MaxFS问题的数据来克服第二个限制。此外，它生成的假设只有前n个排名的子集的基础上匹配分数和数据拟合残差。这种MaxFS问题的数据减少使得算法在计算上是现实的。本文提出的带内点尺度估计和子集更新的迭代MaxFS（IMaxFS-ISE-SU）算法，通过交替进行假设生成和拟合，直到找到所有的真实结构，该算法生成的假设比基于随机抽样的方法可靠得多，特别是当（伪）离群值比率增加时。实验结果表明，我们的方法可以产生更可靠和一致的假设比随机抽样为基础的方法估计多个结构的数据与许多离群值。
英文摘要：
We present an efficient deterministic hypothesis generation algorithm for robust fitting of multiple structures based on the maximum feasible subsystem (MaxFS) framework. Despite its advantage, a global optimization method such as MaxFS has two main limitations for geometric model fitting. First, its performance is much influenced by the user-specified inlier scale. Second, it is computationally inefficient for large data. The presented MaxFS-based algorithm iteratively estimates model parameters and inlier scale and also overcomes the second limitation by reducing data for the MaxFS problem. Further it generates hypotheses only with top-n ranked subsets based on matching scores and data fitting residuals. This reduction of data for the MaxFS problem makes the algorithm computationally realistic. Our method, called iterative MaxFS with inlier scale estimation and subset updating (IMaxFS-ISE-SU) in this paper, performs hypothesis generation and fitting alternately until all of true structures are found. The IMaxFS-ISE-SU algorithm generates substantially more reliable hypotheses than random sampling-based methods especially as (pseudo-)outlier ratios increase. Experimental results demonstrate that our method can generate more reliable and consistent hypotheses than random sampling-based methods for estimating multiple structures from data with many outliers.
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