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We present a variational method for subdivision surface reconstruction from a noisy dense mesh. A new set of subdivision rules with continuous sharpness control is introduced into Loop subdivision for better modeling subdivision surface features such as semi-sharp creases, creases, and corners. The key idea is to assign a sharpness value to each edge of the control mesh to continuously control the surface features. Based on the new subdivision rules, a variational model with L1 norm is formulated to find the control mesh and the corresponding sharpness values of the subdivision surface that best fits the input mesh. An iterative solver based on the augmented Lagrangian method and particle swarm optimization is used to solve the resulting non-linear, non-differentiable optimization problem. Our experimental results show that our method can handle meshes well with sharp/semi-sharp features and noise.


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Variational reconstruction using subdivision surfaces with continuous sharpness control

Show Author's information Xiaoqun Wu1( )Jianmin Zheng2Yiyu Cai2Haisheng Li1
Beijing Key Lab of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, China.
College of Engineering, Nanyang Technological University, Singapore.

Abstract

We present a variational method for subdivision surface reconstruction from a noisy dense mesh. A new set of subdivision rules with continuous sharpness control is introduced into Loop subdivision for better modeling subdivision surface features such as semi-sharp creases, creases, and corners. The key idea is to assign a sharpness value to each edge of the control mesh to continuously control the surface features. Based on the new subdivision rules, a variational model with L1 norm is formulated to find the control mesh and the corresponding sharpness values of the subdivision surface that best fits the input mesh. An iterative solver based on the augmented Lagrangian method and particle swarm optimization is used to solve the resulting non-linear, non-differentiable optimization problem. Our experimental results show that our method can handle meshes well with sharp/semi-sharp features and noise.

Keywords: variational model, subdivision surface, sharpness, surface reconstruction, L1 norm

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Publication history
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Publication history

Revised: 27 February 2017
Accepted: 27 April 2017
Published: 15 June 2017
Issue date: September 2017

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© The Author(s) 2017

Acknowledgements

The main idea of this paper was presented in the Computational Visual Media Conference 2017. This research was partially supported by the National Natural Science Foundation of China (No. 61602015), an MOE AcRF Tier 1 Grant of Singapore (RG26/15), Beijing Natural Science Foundation (No. 4162019), open funding project of State Key Lab of Virtual Reality Technology and Systems at Beihang University (No. BUAA-VR-16KF-06), and the Research Foundation for Young Scholars of Beijing Technology and Business University.

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