CASIA OpenIR  > 模式识别国家重点实验室  > 三维可视计算
MGCN: Descriptor Learning using Multiscale GCNs
Wang, Yiqun1,2,3; Ren, Jing3; Yan, Dong-Ming1,2; Guo, Jianwei1,2; Zhang, Xiaopeng1,2; Wonka, Peter3
发表期刊ACM TRANSACTIONS ON GRAPHICS
ISSN0730-0301
2020-07-01
卷号39期号:4页码:15
摘要

We propose a novel framework for computing descriptors for characterizing points on three-dimensional surfaces. First, we present a new non-learned feature that uses graph wavelets to decompose the Dirichlet energy on a surface. We call this new feature Wavelet Energy Decomposition Signature (WEDS). Second, we propose a new Multiscale Graph Convolutional Network (MGCN) to transform a non-learned feature to a more discriminative descriptor. Our results show that the new descriptor WEDS is more discriminative than the current state-of-the-art non-learned descriptors and that the combination of WEDS and MGCN is better than the state-of-the-art learned descriptors. An important design criterion for our descriptor is the robustness to different surface discretizations including triangulations with varying numbers of vertices. Our results demonstrate that previous graph convolutional networks significantly overlit to a particular resolution or even a particular triangulation, but MGCN generalizes well to different surface discretizations. In addition, MGCN is compatible with previous descriptors and it can also be used to improve the performance of other descriptors, such as the heat kernel signature, the wave kernel signature, or the local point signature.

关键词Multiscale Energy Decomposition Wavelet Convolution Shape Matching
DOI10.1145/3386569.3392443
收录类别SCI
语种英语
资助项目National Key R&D Program of China[2018YFB2100602] ; National Key R&D Program of China[2019YFB2204104] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[61802406] ; National Natural Science Foundation of China[61972459] ; Beijing Natural Science Foundation[L182059] ; CCF-Tencent Open Research Fund ; Shenzhen Basic Research Program[JCYJ20180507182222355] ; Alibaba Group through Alibaba Innovative Research Program ; KAUST OSR Award[CRG-2017-3426]
项目资助者National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; CCF-Tencent Open Research Fund ; Shenzhen Basic Research Program ; Alibaba Group through Alibaba Innovative Research Program ; KAUST OSR Award
WOS研究方向Computer Science
WOS类目Computer Science, Software Engineering
WOS记录号WOS:000583700300095
出版者ASSOC COMPUTING MACHINERY
七大方向——子方向分类计算机图形学与虚拟现实
引用统计
被引频次:16[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/41656
专题模式识别国家重点实验室_三维可视计算
通讯作者Wang, Yiqun
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch AI, Beijing, Peoples R China
3.KAUST, Thuwal, Saudi Arabia
第一作者单位中国科学院自动化研究所
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang, Yiqun,Ren, Jing,Yan, Dong-Ming,et al. MGCN: Descriptor Learning using Multiscale GCNs[J]. ACM TRANSACTIONS ON GRAPHICS,2020,39(4):15.
APA Wang, Yiqun,Ren, Jing,Yan, Dong-Ming,Guo, Jianwei,Zhang, Xiaopeng,&Wonka, Peter.(2020).MGCN: Descriptor Learning using Multiscale GCNs.ACM TRANSACTIONS ON GRAPHICS,39(4),15.
MLA Wang, Yiqun,et al."MGCN: Descriptor Learning using Multiscale GCNs".ACM TRANSACTIONS ON GRAPHICS 39.4(2020):15.
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