  Recently, Expectation-maximization (EM) algorithm has been introduced as an
effective means to solve multi-view registration problem. Most of the previous
methods assume that each data point is drawn from the Gaussian Mixture Model
(GMM), which is difficult to deal with the noise with heavy-tail or outliers.
Accordingly, this paper proposed an effective registration method based on
Student's t Mixture Model (StMM). More specially, we assume that each data
point is drawn from one unique StMM, where its nearest neighbors (NNs) in other
point sets are regarded as the t-distribution centroids with equal covariances,
membership probabilities, and fixed degrees of freedom. Based on this
assumption, the multi-view registration problem is formulated into the
maximization of the likelihood function including all rigid transformations.
Subsequently, the EM algorithm is utilized to optimize rigid transformations as
well as the only t-distribution covariance for multi-view registration. Since
only a few model parameters require to be optimized, the proposed method is
more likely to obtain the desired registration results. Besides, all
t-distribution centroids can be obtained by the NN search method, it is very
efficient to achieve multi-view registration. What's more, the t-distribution
takes the noise with heavy-tail into consideration, which makes the proposed
method be inherently robust to noises and outliers. Experimental results tested
on benchmark data sets illustrate its superior performance on robustness and
accuracy over state-of-the-art methods.
