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Computational Visual Media

, Volume 3, Issue 3, pp 217–228 | Cite as

Variational reconstruction using subdivision surfaces with continuous sharpness control

  • Xiaoqun Wu
  • Jianmin Zheng
  • Yiyu Cai
  • Haisheng Li
Open Access
Research Article

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 L1norm 

Notes

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. BUAAVR-16KF-06), and the Research Foundation for Young Scholars of Beijing Technology and Business University.

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Copyright information

© The Author(s) 2017

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Authors and Affiliations

  • Xiaoqun Wu
    • 1
  • Jianmin Zheng
    • 2
  • Yiyu Cai
    • 2
  • Haisheng Li
    • 1
  1. 1.Beijing Key Lab of Big Data Technology for Food Safety, School of Computer and Information EngineeringBeijing Technology and Business UniversityBeijingChina
  2. 2.College of EngineeringNanyang Technological UniversitySingaporeSingapore

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