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CVPR
2007
IEEE

A Multi-Scale Tikhonov Regularization Scheme for Implicit Surface Modelling

14 years 5 months ago
A Multi-Scale Tikhonov Regularization Scheme for Implicit Surface Modelling
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 c...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
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