Message passing on the two-layer network for geometric model fitting

Abstract

In this paper, we propose a novel model fitting method to recover multiple geometric structures from data corrupted by noises and outliers. Instead of analyzing each model hypothesis or each data point separately, the proposed method combines both the consensus information in all model hypotheses and the preference information in all data points into a two-layer network, in which the vertices in the first layer represent the data points and the vertices in the second layer represent the model hypotheses. Based on this formulation, the clusters in the second layer of the network, corresponding to the true structures, are detected by using an effective Two-Stage Message Passing (TSMP) algorithm. TSMP can not only accurately detect multiple structures in data without specifying the number of structures, but also handle data even with a large number of outliers. Experimental results on both synthetic data and real images further demonstrate the superiority of the proposed method over several state-of-the-art fitting methods.