Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation

Yonghuai Liu, Luigi De Dominicis, Baogang Wei, Liang Chen, Ralph R. Martin

Research output: Contribution to journalArticle (journal)peer-review

24 Citations (Scopus)
84 Downloads (Pure)


Feature extraction and matching (FEM) for 3D shapes finds numerous applications in computer graphics and vision for object modeling, retrieval, morphing, and recognition. However, unavoidable incorrect matches lead to inaccurate estimation of the transformation relating different datasets. Inspired by AdaBoost, this paper proposes a novel iterative re-weighting method to tackle the challenging problem of evaluating point matches established by typical FEM methods. Weights are used to indicate the degree of belief that each point match is correct. Our method has three key steps: (i) estimation of the underlying transformation using weighted least squares, (ii) penalty parameter estimation via minimization of the weighted variance of the matching errors, and (iii) weight reestimation taking into account both matching errors and information learnt in previous iterations. A comparative study, based on real shapes captured by two laser scanners, shows that the proposed method outperforms four other state-of-the-art methods in terms of evaluating point matches between overlapping shapes established by two typical FEM methods, resulting in more accurate estimates of the underlying transformation. This improved transformation can be used to better initialize the iterative closest point algorithm and its variants, making 3D shape registration more likely to succeed.
Original languageEnglish
Pages (from-to)1058-1071
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number9
Early online date5 Mar 2015
Publication statusPublished - 1 Sept 2015


  • Feature extraction
  • Feature matching
  • Point match evaluation
  • Iterative re-weighting
  • Rigid transformation
  • Registration


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