Learning-Based Intrinsic Reflectional Symmetry Detection

doi:10.1109/TVCG.2022.3172361

	Learning-Based Intrinsic Reflectional Symmetry Detection
	Qiao, Yi-Ling 1,2; Gao, Lin 2,3; Liu, Shu-Zhi 2,3; Liu, Ligang 4; Lai, Yu-Kun 5; Chen, Xilin 6
	2023-09-01
发表期刊	IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
ISSN	1077-2626
卷号	29 期号:9 页码:3799-3808
摘要	Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this article, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we manually label the eigenfunction signs for a variety of shapes and train a novel neural network to predict the sign of each eigenfunction under symmetry. Our network aims at learning the global property of functions and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated bases, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 20 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.
关键词	Mesh processing symmetry detection deep learning intrinsic reflectional symmetry laplacian eigenanalysis
DOI	10.1109/TVCG.2022.3172361
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Natural Science Foundation for Distinguished Young Scholars[JQ21013] ; National Natural Science Foundation of China[62061136007] ; National Natural Science Foundation of China[61872440] ; Royal Society Newton Advanced Fellowship[NAF-R2-192151] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Software Engineering
WOS记录号	WOS:001041912300006
出版者	IEEE COMPUTER SOC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21376
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gao, Lin; Chen, Xilin
作者单位	1.Univ Maryland, College Pk, MD 20742 USA 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 101408, Peoples R China 4.Univ Sci & Technol China, Sch Math Sci, Hefei 230026, Peoples R China 5.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF10 3AT, Wales 6.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Qiao, Yi-Ling,Gao, Lin,Liu, Shu-Zhi,et al. Learning-Based Intrinsic Reflectional Symmetry Detection[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2023,29(9):3799-3808.
APA	Qiao, Yi-Ling,Gao, Lin,Liu, Shu-Zhi,Liu, Ligang,Lai, Yu-Kun,&Chen, Xilin.(2023).Learning-Based Intrinsic Reflectional Symmetry Detection.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,29(9),3799-3808.
MLA	Qiao, Yi-Ling,et al."Learning-Based Intrinsic Reflectional Symmetry Detection".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 29.9(2023):3799-3808.