Robust Estimation of Multiple Inlier Structures

TL;DR

This paper introduces a robust method for independently estimating multiple inlier structures in data, adaptively determining scales without thresholds, and classifying structures by strength, demonstrated through synthetic and real examples.

Contribution

The paper presents a novel robust estimator that processes each structure separately with adaptive scale estimation and no threshold, improving multi-structure data segmentation.

Findings

Effective in synthetic and real data scenarios

Automatically sorts structures by strength

Operates without predefined thresholds

Abstract

The robust estimator presented in this paper processes each structure independently. The scales of the structures are estimated adaptively and no threshold is involved in spite of different objective functions. The user has to specify only the number of elemental subsets for random sampling. After classifying all the input data, the segmented structures are sorted by their strengths and the strongest inlier structures come out at the top. Like any robust estimators, this algorithm also has limitations which are described in detail. Several synthetic and real examples are presented to illustrate every aspect of the algorithm.