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A survey on deep geometry learning: From a representation perspective

文献类型：期刊论文

作者Xiao, Yun-Peng3; Lai, Yu-Kun2; Zhang, Fang-Lue1; Li, Chunpeng3; Gao, Lin3
刊名COMPUTATIONAL VISUAL MEDIA
出版日期2020-06-01
卷号6期号:2页码:113-133
关键词3D shape representation geometry learning neural networks computer graphics
ISSN号2096-0433
DOI10.1007/s41095-020-0174-8
英文摘要Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions.
资助项目National Natural Science Foundation of China[61828204] ; National Natural Science Foundation of China[61872440] ; Beijing Municipal Natural Science Foundation[L182016] ; Youth Innovation Promotion Association CAS ; CCF-Tencent Open Fund ; Royal Society-Newton Advanced Fellowship[NAF\R2\192151] ; Royal Society[IES\R1\180126]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000648691300001
出版者SPRINGERNATURE
源URL[http://119.78.100.204/handle/2XEOYT63/17772]  
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.Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand
2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales
3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Yun-Peng,Lai, Yu-Kun,Zhang, Fang-Lue,et al. A survey on deep geometry learning: From a representation perspective[J]. COMPUTATIONAL VISUAL MEDIA,2020,6(2):113-133.
APA Xiao, Yun-Peng,Lai, Yu-Kun,Zhang, Fang-Lue,Li, Chunpeng,&Gao, Lin.(2020).A survey on deep geometry learning: From a representation perspective.COMPUTATIONAL VISUAL MEDIA,6(2),113-133.
MLA Xiao, Yun-Peng,et al."A survey on deep geometry learning: From a representation perspective".COMPUTATIONAL VISUAL MEDIA 6.2(2020):113-133.

入库方式： OAI收割

来源：计算技术研究所

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