A Revisit of Shape Editing Techniques: From the Geometric to the Neural Viewpoint

Yu-Jie Yuan; Yukun Lai; Tong Wu; Lin Gao; Li-Gang Liu

doi:10.1007/s11390-021-1414-9

Volume 36 Issue 3

May 2021

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Journal of Computer Science and Technology > 2021 > 36(3): 520-554. > DOI: 10.1007/s11390-021-1414-9

Yu-Jie Yuan, Yukun Lai, Tong Wu, Lin Gao, Li-Gang Liu. A Revisit of Shape Editing Techniques: From the Geometric to the Neural Viewpoint[J]. Journal of Computer Science and Technology, 2021, 36(3): 520-554. DOI: 10.1007/s11390-021-1414-9

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A Revisit of Shape Editing Techniques: From the Geometric to the Neural Viewpoint

Yu-Jie Yuan^1,2 Student Member, IEEE,
Yukun Lai³ Member, IEEE,
Tong Wu^1,2 Student Member, CCF,
Lin Gao^1,2, , Member, CCF, ACM,
Li-Gang Liu⁴ Member, CCF, ACM, IEEE

1 Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, U.K.;
4 School of Mathematical Sciences, University of Science and Technology of China, Hefei 230026, China

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 62061136007 and 61872440, the Royal Society Newton Advanced Fellowship under Grant No. NAF\R2\192151, Youth Innovation Promotion Association CAS, and Science and Technology Service Network Initiative, Chinese Academy of Sciences under Grant No. KFJ-STS-QYZD-2021-11-001.

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Author Bio:
Yu-Jie Yuan received his Bachelor's degree in mathematics from Xi'an Jiaotong University, Xi'an, in 2018. He is currently a Ph.D. candidate in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing. His research interests include computer graphics and geometric processing.
Corresponding author:
Lin Gao E-mail: gaolin@ict.ac.cn
Received Date: March 01, 2021
Revised Date: April 21, 2021
Published Date: May 04, 2021

Abstract

Abstract

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy formulation. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which are naturally data-driven. We mainly survey recent research studies from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed.
- mesh deformation,
- man-made model editing,
- deformation representation,
- optimization,
- deep learning

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