Point Set Registration with a Hybrid Structure Constraint

Jing Sun, Xia Chen, Zhan Li Sun, Kin Man Lam, Zhigang Zeng

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Due to some unfavorable factors, how to accurately register point sets is still a challenging task. In this paper, an effective point set registration approach is proposed based on a hybrid structure constrain. In the proposed method, a composite weight coefficient is determined based on the amplitudes of the vector and the corresponding projection of neighbor points. Given the composite weight coefficient, a local structure constraint is constructed as a linear combination of the vectors of neighbor points. A Gaussian mixture model is established by utilizing the local structure constraint and a global structure constraint based on the motion coherence theory. In addition, an expectation-maximization algorithm is derived to solve the unknown variables in the proposed model. For the constraint terms, an update strategy is utilized to obtain the approximately optimal weight coefficients. Compared to the state-of-the-art approaches, the proposed model is more robust due to the use of multiple effective constraints. Experimental results on some widely used data sets demonstrate the effectiveness of the proposed model.

Original languageEnglish
Article number8847430
Pages (from-to)164246-164255
Number of pages10
JournalIEEE Access
Publication statusPublished - Sept 2019


  • expectation-maximization algorithm
  • Gaussian mixture model
  • Point set registration
  • structure constraint

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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