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The normal distributions transform (NDT) is an effective paradigm for thepoint set registration. This method is originally designed for pair-wiseregistration and it will suffer from great challenges when applied tomulti-view registration. Under the NDT framework, this paper proposes a novelmulti-view registration method, named 3D multi-view registration based on thenormal distributions transform (3DMNDT), which integrates the K-meansclustering and Lie algebra solver to achieve multi-view registration. Morespecifically, the multi-view registration is cast into the problem of maximumlikelihood estimation. Then, the K-means algorithm is utilized to divide alldata points into different clusters, where a normal distribution is computed tolocally models the probability of measuring a data point in each cluster.Subsequently, the registration problem is formulated by the NDT-basedlikelihood function. To maximize this likelihood function, the Lie algebrasolver is developed to sequentially optimize each rigid transformation. Theproposed method alternately implements data point clustering, NDT computing,and likelihood maximization until desired registration results are obtained.Experimental results tested on benchmark data sets illustrate that the proposedmethod can achieve state-of-the-art performance for multi-view registration.

3DMNDT:3D multi-view registration method based on the normal distributions transform