A survey on deep geometry learning: From a representation perspective | |
Xiao, Yun-Peng3; Lai, Yu-Kun2; Zhang, Fang-Lue1; Li, Chunpeng3; Gao, Lin3 | |
刊名 | COMPUTATIONAL VISUAL MEDIA |
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 |
DOI | 10.1007/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 |
出版者 | SPRINGERNATURE |
WOS记录号 | WOS:000648691300001 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/17772] |
专题 | 中国科学院计算技术研究所 |
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. |
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