Hierarchical K-means clustering for registration of multi-view point sets. (September 2021)

• Record Type: Journal Article
• Authors: Guo, Rui; Chen, Jinqian; Wang, Lin
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Multi-view registration -- K-means clustering -- Iterative closest point -- Point set registration; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107321
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: As a long-standing research issue in computer vision and robotics, multi-view registration has attracted much attention in recent years. Most existing works are mainly focus on the estimating the point to point match correspondence, which usually suffers from the poor initial pose and data noise as well as leads to the inaccurate matches. To overcome the aforementioned limitation, we propose a novel Hierarchical K-means Clustering Registration (HKCR), which casts the multi-view registration as a hierarchical clustering task. Specifically, the proposed method employs a small number of clusters firstly, then increases the number of clusters during the registration process. Benefiting from the recursive partitioning process, more robust and more accurate results can be achieved with the increasing finer granularity. To show the effectiveness and robustness of HKCR, extensive experiments are conducted on several benchmark datasets and compared to several state-of-the-art methods.