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    General discriminative optimization for point set registration

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    Publication date
    2022-02
    End of Embargo
    2022-11-17
    Author
    Zhao, Y.
    Tang, W.
    Feng, J.
    Wan, Tao Ruan
    Xi, L.
    Keyword
    Point set registration
    Supervised learning
    Learning-based optimisation
    Rights
    © 2022 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
    Peer-Reviewed
    Yes
    Open Access status
    embargoedAccess
    
    Metadata
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    Abstract
    Point set registration has been actively studied in computer vision and graphics. Optimization algorithms are at the core of solving registration problems. Traditional optimization approaches are mainly based on the gradient of objective functions. The derivation of objective functions makes it challenging to find optimal solutions for complex optimization models, especially for those applications where accuracy is critical. Learning-based optimization is a novel approach to address this problem, which learns the gradient direction from datasets. However, many learning-based optimization algorithms learn gradient directions via a single feature extracted from the dataset, which will cause the updating direction to be vulnerable to perturbations around the data, thus falling into a bad stationary point. This paper proposes the General Discriminative Optimization (GDO) method that updates a gradient path automatically through the trade-off among contributions of different features on updating gradients. We illustrate the benefits of GDO with tasks of 3D point set registrations and show that GDO outperforms the state-of-the-art registration methods in terms of accuracy and robustness to perturbations.
    URI
    http://hdl.handle.net/10454/18830
    Version
    Accepted manuscript
    Citation
    Zhao Y, Tang W, Feng J et al (2022) General discriminative optimization for point set registration. Computers and Graphics. 102: 521-532.
    Link to publisher’s version
    https://doi.org/10.1016/j.cag.2021.11.001
    Type
    Article
    Notes
    The full-text of this article will be released for public view at the end of the publisher embargo on 17 Nov 2022.
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    Engineering and Informatics Publications

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