A multi-scale Tikhonov regularization scheme for implicit surface modelling
Refereed conference paper presented and published in conference proceedings


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AbstractKernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape models from large-scale sets of point cloud samples efficiently. In this paper, we propose a fast solution for approximating implicit surfaces based on a multi-scale Tikhonov regularization scheme. The optimization of our scheme is formulated into a sparse linear equation system, which can be efficiently solved by factorization methods. Different from traditional approaches, our scheme does not employ auxiliary off-surface points, which not only saves the computational cost but also avoids the problem of injected noise. To further speedup our solution, we present a multi-scale surface fitting algorithm of coarse to fine modelling. We conduct comprehensive experiments to evaluate the performance of our solution on a number of datasets of different scales. The promising results show that our suggested scheme is considerably more efficient than the state-of-the-art approach.
All Author(s) ListZhu J, Hoi SCH, Lyu MR
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Start Date of Conference17/06/2007
End Date of Conference22/06/2007
Place of ConferenceMinneapolis
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE Computer Society,
Year2007
Month1
Day1
PublisherIEEE
Pages399 - 405
ISBN978-1-4244-1179-5
ISSN1063-6919
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Mathematical & Computational Biology; Remote Sensing

Last updated on 2020-20-05 at 00:29