Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation

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

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)
245 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
re-estimation 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
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number9
Publication statusPublished - 04 Mar 2015


  • feature extraction
  • feature matching
  • point match evaluation
  • iterative re-weighting
  • registration
  • rigid tranformation


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