中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

文献类型：期刊论文

作者Gao, Lin4,5; Zhang, Ling-Xiao4,5; Meng, Hsien-Yu3; Ren, Yi-Hui4,5; Lai, Yu-Kun2; Kobbelt, Leif1
刊名IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
出版日期2021-06-01
卷号27期号:6页码:3007-3018
关键词Three-dimensional displays Shape Geometry Two dimensional displays Feature extraction Solid modeling Computational modeling Unsupervised learning convolutional neural network symmetry detection 3D models planar reflective symmetry
ISSN号1077-2626
DOI10.1109/TVCG.2020.3003823
英文摘要In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this article, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.
资助项目National Natural Science Foundation of China[61872440] ; National Natural Science Foundation of China[61828204] ; Beijing Municipal Natural Science Foundation[L182016] ; Royal Society Newton Advanced Fellowship[NAF\R2\192151] ; Youth Innovation Promotion Association CAS ; CCF-Tencent Open Fund ; Tencent AI Lab Rhino-Bird Focused Research Program[JR202024] ; Open Project Program of the National Laboratory of Pattern Recognition[201900055]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000649620700018
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/17680]  
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
作者单位1.Rhein Westfal TH Aachen, Inst Comp Graph & Multimedia, D-52062 Aachen, Germany
2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, Wales
3.Univ Maryland, College Pk, MD 20742 USA
4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Lin,Zhang, Ling-Xiao,Meng, Hsien-Yu,et al. PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2021,27(6):3007-3018.
APA Gao, Lin,Zhang, Ling-Xiao,Meng, Hsien-Yu,Ren, Yi-Hui,Lai, Yu-Kun,&Kobbelt, Leif.(2021).PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,27(6),3007-3018.
MLA Gao, Lin,et al."PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 27.6(2021):3007-3018.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。