Fuzzy Correspondences and Kernel Density Estimation for Contaminated Point Set Registration

Abstract

Point set registration problem is challenging to solve in the presence of outliers. In this paper, we proposed a registration method based on fuzzy correspondences and kernel density estimation. The main idea of our method is that the moving point set consists of inliers represented using a mixture of Gaussian, and outliers represented via an additional uniform distribution, then we use the fuzzy correspondences to estimate the Gaussian elements in the mixture model. There are four parts of the paper: we formulate the contaminated point set registration problem as a mixture model according to the well known Gaussian mixture model (GMM) based method firstly. Secondly, Gaussian elements are estimated by fuzzy correspondences to increase the registration accuracy efficiently. Thirdly, the optimal transformation between two contaminated point sets is expressed by representation theorem, and solved by EM algorithm iteratively. Finally, we compare our proposed method with several state-of-the-art methods, and the results show that our method gets better performances than the other methods in most tested scenarios.