  Generally, there are three main factors that determine the practical
usability of registration, i.e., accuracy, robustness, and efficiency. In
real-time applications, efficiency and robustness are more important. To
promote these two abilities, we cast the multi-view registration into a
clustering task. All the centroids are uniformly sampled from the initially
aligned point sets involved in the multi-view registration, which makes it
rather efficient and effective for the clustering. Then, each point is assigned
to a single cluster and each cluster centroid is updated accordingly.
Subsequently, the shape comprised by all cluster centroids is used to
sequentially estimate the rigid transformation for each point set. For accuracy
and stability, clustering and transformation estimation are alternately and
iteratively applied to all point sets. We tested our proposed approach on
several benchmark datasets and compared it with state-of-the-art approaches.
Experimental results validate its efficiency and robustness for the
registration of multi-view point sets.
