A survey on deep geometry learning:From a representation perspective

作     者：Yun-Peng Xiao Yu-Kun Lai Fang-Lue Zhang Chunpeng Li Lin Gao Yun-Peng Xiao;Yu-Kun Lai;Fang-Lue Zhang;Chunpeng Li;Lin Gao

作者机构：Institute of Computing TechnologyChinese Academy of SciencesBeijingChina School of Computer Science and InformaticsCardiff UniversityWalesUK School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2020年第6卷第2期

页      面：113-133页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(61828204,61872440) Beijing Municipal Natural Science Foundation(L182016) Youth Innovation Promotion Association CAS,CCF-Tencent Open Fund Royal Society Newton Advanced Fellowship(NAF\R2\192151) the Royal Society(IES\R1\180126) 

主　　题：3D shape representation geometry learning neural networks computer graphics 

摘      要：Researchers have achieved great success in dealing with 2 D images using deep *** recent years,3 D computer vision and geometry deep learning have gained ever more *** advanced techniques for 3 D shapes have been proposed for different *** 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,*** performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all ***,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different *** also present existing datasets in these representations and further discuss future research directions.