Outliers Removed via Spectral Clustering for Robust Model Fitting

This paper proposes a robust model fitting method, called Outliers Removed via Spectral Clustering (ORSC), to estimate multiple inlier structures in the presence of a large number of outliers. The basic idea is to cast each data point to the conceptual space, where the distance distribution of inliers and outliers from the origin is significantly different. Therefore, all the points can be classified into inliers and outliers according to the distribution of points of each subspace, which is obtained by a spectral clustering algorithm. Furthermore, we can use the clustering result to guide the follow-up sampling to get more clean data points for hypotheses generation when handling the complex multi-structure model with a large proportion of outliers. Experimental results show that our method achieves superior performance compared with some state-of-the-art methods in terms of line fitting, circle fitting, and real images.

I. Introduction

As a fundamental research task, model fitting plays an important role in the field of machine vision. It has extensive applications in visual SLAM [1], motion segmentation [2], [3], 3D reconstruction [4], and panoramic photography [5] etc. In practice, due to the presence of a large number of outliers in images or videos taken by the sensors, it is still a challenging task to estimate the number of models and their corresponding parameters accurately in the real-world data.