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Generally, there are three main factors that determine the practicalusability of registration, i.e., accuracy, robustness, and efficiency. Inreal-time applications, efficiency and robustness are more important. Topromote these two abilities, we cast the multi-view registration into aclustering task. All the centroids are uniformly sampled from the initiallyaligned point sets involved in the multi-view registration, which makes itrather efficient and effective for the clustering. Then, each point is assignedto a single cluster and each cluster centroid is updated accordingly.Subsequently, the shape comprised by all cluster centroids is used tosequentially estimate the rigid transformation for each point set. For accuracyand stability, clustering and transformation estimation are alternately anditeratively applied to all point sets. We tested our proposed approach onseveral benchmark datasets and compared it with state-of-the-art approaches.Experimental results validate its efficiency and robustness for theregistration of multi-view point sets.

K-means clustering for efficient and robust registration of multi-view point sets