A consensus sampling technique for fast and robust model fitting

In this paper, a new algorithm is proposed to improve the efficiency and robustness of random sampling consensus (RANSAC) without prior information about the error scale. Three techniques are developed in an iterative hypothesis-and-evaluation framework. Firstly, we propose a consensus sampling technique to increase the probability of sampling inliers by exploiting the feedback information obtained from the evaluation procedure. Secondly, the preemptive multiple K-th order approximation (PMKA) is developed for efficient model evaluation with unknown error scale. Furthermore, we propose a coarse-to-fine strategy for the robust standard deviation estimation to determine the unknown error scale. Experimental results of the fundamental matrix computation on both simulated and real data are shown to demonstrate the superiority of the proposed algorithm over the previous methods.

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