  The normal distributions transform (NDT) is an effective paradigm for the
point set registration. This method is originally designed for pair-wise
registration and it will suffer from great challenges when applied to
multi-view registration. Under the NDT framework, this paper proposes a novel
multi-view registration method, named 3D multi-view registration based on the
normal distributions transform (3DMNDT), which integrates the K-means
clustering and Lie algebra solver to achieve multi-view registration. More
specifically, the multi-view registration is cast into the problem of maximum
likelihood estimation. Then, the K-means algorithm is utilized to divide all
data points into different clusters, where a normal distribution is computed to
locally models the probability of measuring a data point in each cluster.
Subsequently, the registration problem is formulated by the NDT-based
likelihood function. To maximize this likelihood function, the Lie algebra
solver is developed to sequentially optimize each rigid transformation. The
proposed 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 proposed
method can achieve state-of-the-art performance for multi-view registration.
