Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2007

Abstract

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.

Keywords

Algorithms, Factorization, Linear equations, Problem solving, Support vector machines, Tikhonov regularization, Kernel machines

Discipline

Computer Sciences | Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Conference on Computer Vision and Pattern Recognition CVPR 2007: Minneapolis, MN, June 17-22: Proceedings

First Page

4270047-1

Last Page

7

ISBN

9781424411795

Identifier

10.1109/CVPR.2007.383022

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CVPR.2007.383022

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