  Registration of multi-view point sets is a prerequisite for 3D model
reconstruction. To solve this problem, most of previous approaches either
partially explore available information or blindly utilize unnecessary
information to align each point set, which may lead to the undesired results or
introduce extra computation complexity. To this end, this paper consider the
multi-view registration problem as a maximum likelihood estimation problem and
proposes a novel multi-view registration approach under the perspective of
Expectation-Maximization (EM). The basic idea of our approach is that different
data points are generated by the same number of Gaussian mixture models (GMMs).
For each data point in one point set, its nearest neighbors can be searched
from other well-aligned point sets. Then, we can suppose this data point is
generated by the special GMM, which is composed of each nearest neighbor
adhered with one Gaussian distribution. Based on this assumption, it is
reasonable to define the likelihood function including all rigid
transformations, which requires to be estimated for multi-view registration.
Subsequently, the EM algorithm is utilized to maximize the likelihood function
so as to estimate all rigid transformations. Finally, the proposed approach is
tested on several bench mark data sets and compared with some state-of-the-art
algorithms. Experimental results illustrate its super performance on accuracy,
robustness and efficiency for the registration of multi-view point sets.
