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


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摘要Kernel 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.
著者Zhu J, Hoi SCH, Lyu MR
會議名稱IEEE Conference on Computer Vision and Pattern Recognition
會議開始日17.06.2007
會議完結日22.06.2007
會議地點Minneapolis
會議國家/地區美國
詳細描述organized by IEEE Computer Society,
出版年份2007
月份1
日期1
出版社IEEE
頁次399 - 405
國際標準書號978-1-4244-1179-5
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Software Engineering; Imaging Science & Photographic Technology; Mathematical & Computational Biology; Remote Sensing

上次更新時間 2020-09-07 於 03:53