Abstract
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.
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 language | English |
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Pages (from-to) | 1058-1071 |
Number of pages | 14 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Volume | 21 |
Issue number | 9 |
DOIs | |
Publication status | Published - 04 Mar 2015 |
Keywords
- feature extraction
- feature matching
- point match evaluation
- iterative re-weighting
- registration
- rigid tranformation