计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 47-58.doi: 10.11896/j.issn.1002-137X.2019.09.006

• 综述 • 上一篇    下一篇

基于深度学习的三维形状特征提取方法

周燕, 曾凡智, 吴臣, 罗粤, 刘紫琴   

  1. (佛山科学技术学院计算机系 广东 佛山528000)
  • 收稿日期:2019-05-15 出版日期:2019-09-15 发布日期:2019-09-02
  • 通讯作者: 周 燕(1979-),女,硕士,教授,CCF会员,主要研究方向为计算机视觉、三维模型检索,E-mail:zhouyan791266@163.com
  • 作者简介:曾凡智(1965-),男,博士,教授,主要研究方向为计算机视觉、图像处理、数据挖掘;吴 臣(1974-),男,硕士,讲师,主要研究方向为模式识别、智能计算;罗 粤(1994-),男,硕士生,主要研究方向为计算机视觉、三维模型检索;刘紫琴(1995-),女,硕士生,主要研究方向为计算机视觉、视频分析。
  • 基金资助:
    国家自然科学基金(61602116),广东省自然科学基金(2017A030313388),广东省工程技术研究中心(G601624),佛山市工程技术研究中心(2017GA00015,2016GA10156)

3D Shape Feature Extraction Method Based on Deep Learning

ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin   

  1. (Department of Computer Science,FoShan University,Foshan,Guangdong 528000,China)
  • Received:2019-05-15 Online:2019-09-15 Published:2019-09-02

摘要: 研究具有低维、高鉴别力的三维形状特征提取方法有助于解决三维形状数据分类和检索等问题。随着深度学习的持续发展,结合深度学习的三维形状特征提取方法已成为研究热点。将深度学习与传统的三维形状特征提取方法相结合,不仅可以突破非深度学习方法的瓶颈,而且可以提高三维形状数据分类、检索等任务的准确率,尤其是当三维形状是非刚体时。然而,深度学习尚在发展中,仍存在需要大量训练样本的问题,因此如何运用深度学习方法来高效提取三维形状特征成为了计算机视觉领域的研究重点和难点。目前,研究者大多从改进网络结构和训练方法等方面入手,着重提高神经网络提取特征的能力。文中结合深度学习和三维形状特征提取方法的发展历程,首先介绍相关深度学习模型,以及网络改进、训练方法等方面的新思路;其次重点对基于深度学习的刚体与非刚体的特征提取方法做综合的阐述,描述当前深度学习方法用于三维形状特征提取的情况;然后简述现有三维形状检索系统的现况以及相似度计算方法;最后介绍当前三维形状特征提取方法存在的问题,并探讨其未来的发展趋势。

关键词: 三维形状, 特征提取, 深度学习, 神经网络

Abstract: Research on extracting 3D shape features with low dimension and high discriminating ability can solve the problem such as classification,retrieval of 3D shape data.With the continuous development of deep learning,3D shape feature extraction method combineds with deep learning has become a research hotspot.Combining deep learning with traditional 3D shape feature extraction methods can not only break through the bottleneck of non-deep learningme-thods,but also improve the accuracy of 3D shape data classification,retrieval and other tasks,especially when 3D shape is non-rigid body.However,deep learning is still developing,and there are still problems that require a large number of training samples.Therefore,how to effectively extract 3D shape features by using deep learning methods has become the research focus and difficulty in the field of computer vision.At present,most researchers focus on improving the ability of neural network to extract features by improving network structure,training methods and other aspects.First,the re-levant deep learning model are introduced,and there are some new ideas about the network improvement and training methods.Second,the feature extraction methods of rigid body and non-rigid body based on deep learning are comprehensively expounded which combined with the development of deep learning and 3D shape feature extraction methods,and the current deep learning methods for the 3D shape feature extraction are described.And then,the current situation of the existing 3D shape retrieval system and the similarity calculation method are described.Finally,the current problems of 3D shape feature extraction methods are introduced,and its future development trend are explored.

Key words: 3D shape, Feature extraction, Deep learning, Neural networks

中图分类号: 

  • TP391.4
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