中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
TPN: TOPOLOGICAL PERCEPTION NETWORK FOR 3D MESH REPRESENTATION

文献类型:会议论文

作者Ma BT(马兵涛)1,2,3; Cong Y(丛杨)1,3; Liu HS(刘洪森)1,2,3; Tang X(唐旭)1,3
出版日期2020
会议日期October 25-28, 2020
会议地点Virtual Conference
关键词3D mesh classification and retrieval Topological attention
页码2711-2715
英文摘要As an important type of geometric data for 3D shapes, the unique topological connection makes the mesh more powerful than other types of data, but it also introduces complexity and irregularity. In this paper, we propose a Topological Perception Network (TPN) that consumes meshes directly to learn 3D shape representation via informative topology property. More specifically, to tackle the complexity and irregularity problem, a Topological Perception Attention (TPA) is designed that could incorporate local topological information efficiently via focusing on more important edges of the local topological neighborhood. Meanwhile, it could be stacked to produce global shape representation. Compared with the state-of-the-art, the proposed TPN uses less than half of the vertex number to get better performance, while costing less memory and computational time. Experiments on Model-Net40 and ShapeNet Core55 datasets demonstrate the effectiveness of our method on classification and retrieval.
源文献作者The Institute of Electrical and Electronics Engineers Signal Processing Society
产权排序1
会议录2020 IEEE International Conference on Image Processing (ICIP 2020)
会议录出版者IEEE
会议录出版地New York
语种英语
ISBN号978-1-7281-6395-6
WOS记录号WOS:000646178502164
源URL[http://ir.sia.cn/handle/173321/27996]  
专题沈阳自动化研究所_机器人学研究室
通讯作者Cong Y(丛杨)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.University of Chinese Academy of Sciences,100049, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
推荐引用方式
GB/T 7714
Ma BT,Cong Y,Liu HS,et al. TPN: TOPOLOGICAL PERCEPTION NETWORK FOR 3D MESH REPRESENTATION[C]. 见:. Virtual Conference. October 25-28, 2020.

入库方式: OAI收割

来源:沈阳自动化研究所

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