Deep Learning for Digital Geometry Processing and Analysis: A Review

被引：0
|
作者：
Xia Q. [1 ]
Li S. [1 ]
Hao A. [1 ]
Zhao Q. [1 ]
机构：
[1] State Key Laboratory of Virtual Reality Technology and Systems (Beihang University), Beijing
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2019年 / 56卷 / 01期
关键词：
Computer graphics; Deep learning; Digital geometry processing and analysis; Neural networks; Research progress review;
D O I：
10.7544/issn1000-1239.2019.20180709
中图分类号：
学科分类号：
摘要：
With the rapid development of various hardware sensors and reconstruction technologies, digital geometric models have become the fourth generation of digital multimedia after audio, image and video, and have been widely used in many fields. Traditional digital geometry processing and analysis are mainly based on manually defined features that can only be valid for specific problems or under specific conditions. The deep learning, especially the neural network model, in the success of natural language processing and image processing demonstrates its powerful ability as a feature extraction tool for data analysis, and is therefore gradually used in the field of digital geometry processing. In this paper, we review the works of digital geometry processing and analysis based on deep learning in recent years, carefully analyze the research progress of shape matching and retrieval, shape classification and segmentation, shape generation, shape completion and reconstruction and shape deformation and editing, and also point out some existing problems and a few possible directions of future works. © 2019, Science Press. All right reserved.
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页码：155 / 182
页数：27
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