A survey on deep geometry learning: From a representation perspective

doi:10.1007/s41095-020-0174-8

	A survey on deep geometry learning: From a representation perspective
	Xiao, Yun-Peng 1; Lai, Yu-Kun 2; Zhang, Fang-Lue 3; Li, Chunpeng 1; Gao, Lin 1
	2020-06-01
发表期刊	COMPUTATIONAL VISUAL MEDIA
ISSN	2096-0433
卷号	6 期号:2 页码:113-133
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.
关键词	3D shape representation geometry learning neural networks computer graphics
DOI	10.1007/s41095-020-0174-8
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Software Engineering
WOS记录号	WOS:000648691300001
出版者	SPRINGERNATURE
引用统计	被引频次：60[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/2XEOYT63/17773
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.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales 3.Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington, New Zealand
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.