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深度学习在基于单幅图像的物体三维重建中的应用

陈加 张玉麒 宋鹏 魏艳涛 王煜

陈加, 张玉麒, 宋鹏, 魏艳涛, 王煜. 深度学习在基于单幅图像的物体三维重建中的应用. 自动化学报, 2019, 45(4): 657-668. doi: 10.16383/j.aas.2018.c180236
引用本文: 陈加, 张玉麒, 宋鹏, 魏艳涛, 王煜. 深度学习在基于单幅图像的物体三维重建中的应用. 自动化学报, 2019, 45(4): 657-668. doi: 10.16383/j.aas.2018.c180236
CHEN Jia, ZHANG Yu-Qi, SONG Peng, WEI Yan-Tao, WANG Yu. Application of Deep Learning to 3D Object Reconstruction From a Single Image. ACTA AUTOMATICA SINICA, 2019, 45(4): 657-668. doi: 10.16383/j.aas.2018.c180236
Citation: CHEN Jia, ZHANG Yu-Qi, SONG Peng, WEI Yan-Tao, WANG Yu. Application of Deep Learning to 3D Object Reconstruction From a Single Image. ACTA AUTOMATICA SINICA, 2019, 45(4): 657-668. doi: 10.16383/j.aas.2018.c180236

深度学习在基于单幅图像的物体三维重建中的应用

doi: 10.16383/j.aas.2018.c180236
基金项目: 

国家自然科学基金 61605054

湖北省自然科学基金 2014CFB659

华中师范大学中央高校基本科研业务费 CCNU16JYKX039

华中师范大学中央高校基本科研业务费 CC NU19QD007

华中师范大学中央高校基本科研业务费 CCNU15A0 5023

华中师范大学中央高校基本科研业务费 CCNU19TD007

国家自然科学基金 61502195

详细信息
    作者简介:

    陈加  华中师范大学教育信息技术学院讲师.主要研究方向为可视计算, 运动捕捉, 三维重建, 教育信息技术.E-mail:jc@mail.ccnu.edu.cn

    张玉麒  华中师范大学教育信息技术学院硕士研究生.主要研究方向为深度学习, 三维重建.E-mail:ZYQ2046@mail.ccnu.edu.cn

    宋鹏  瑞士联邦理工学院(洛桑)计算机图形学与几何实验室博士后.主要研究方向为计算机图形学, 三维重建.E-mail:peng.song@epfl.ch

    王煜  香港科技大学机器人研究院院长, 教授.主要研究方向为几何建模与设计, 机器人学.E-mail:mywang@ust.hk

    通讯作者:

    魏艳涛  华中师范大学教育信息技术学院副教授.主要研究方向为深度学习, 计算机视觉.本文通信作者.E-mail:weiyantaoccnu@163.com

  • 本文责任编委 吴毅红

Application of Deep Learning to 3D Object Reconstruction From a Single Image

Funds: 

National Natural Science Foundation of China 61605054

Hubei Provincial Natural Science Foundation 2014CFB659

the Fundamental Research Funds for the central Universities of Central China Normal University CCNU16JYKX039

the Fundamental Research Funds for the central Universities of Central China Normal University CC NU19QD007

the Fundamental Research Funds for the central Universities of Central China Normal University CCNU15A0 5023

the Fundamental Research Funds for the central Universities of Central China Normal University CCNU19TD007

National Natural Science Foundation of China 61502195

More Information
    Author Bio:

     Lecturer at the School of Educational Information Technology, Central China Normal University. His research interest covers visual computing, motion capture, 3D reconstruction, and educational information technology

     Master student at the School of Educational Information Technology, Central China Normal University. His research interest covers deep learning and 3D reconstruction

     Postdoctor at the Computer Graphics and Geometry Laboratory, École Polytechnique Fédérale de Lausanne, Switzerland. His research interest covers computer graphics and 3D reconstruction

     Director (professor) at the Robotics Institute, the Hong Kong University of Science and Technology. His research interest covers geometric modeling and design, robotics

    Corresponding author: WEI Yan-Tao  Associate professor at the School of Educational Information Technology, Central China Normal University. His research interest covers deep learning and computer vision. Corresponding author of this paper
  • 摘要: 基于单幅图像的物体三维重建是计算机视觉领域的一个重要问题,近几十年来得到了广泛的关注.随着深度学习的不断发展,近年来基于单幅图像的物体三维重建取得了显著进展.本文对深度学习在基于单幅图像的物体三维重建领域的研究进展及具体应用进行了综述.首先介绍了基于单幅图像的三维重建的研究背景及其传统方法的研究现状,其次简要介绍了深度学习并详细综述了深度学习在基于单幅图像的物体三维重建中的应用,随后简要概述了三维物体重建的常用公共数据集,最后进行了分析与总结,指出了目前存在的问题及未来的研究方向.
    1)  本文责任编委 吴毅红
  • 表  1  不同方法对PASCAL VOC数据集图像中的物体重建的结果对比[20]

    Table  1  Comparison of different methods on the PASCAL VOC [20]

    方法 飞机 单车 轮船 公交 汽车 椅子 摩托 沙发 火车 电视 均值
    Twarog等[39] 9.73 10.39 11.68 15.40 11.77 8.58 8.99 8.62 23.68 9.45 11.83
    Vicente等[19] 5.07 6.03 8.80 8.76 4.38 5.74 4.86 6.49 17.52 8.37 7.60
    Kar等[20] 5.00 6.27 9.94 6.22 5.18 5.20 4.98 6.58 12.60 9.64 7.16
    下载: 导出CSV

    表  2  现有的传统方法与3D-R2N2重建结果的对比[20]

    Table  2  Comparison of traditional methods and 3D-R2N2 [8]

    方法 飞机 单车 轮船 公交 汽车 椅子 摩托 沙发 火车 电视 均值
    Kar等[20] 0.298 0.114 0.188 0.501 0.472 0.234 0.361 0.149 0.249 0.492 0.318
    Choy等[8] 0.544 0.499 0.560 0.816 0.699 0.280 0.649 0.332 0.672 0.574 0.571
    下载: 导出CSV

    表  3  不同方法以平均IoU值作为评价标准的重建精度对比

    Table  3  3D reconstruction comparison with different methods using IoU

    Choy等[8] Yan等[79] Kar等[74] Fan等[74] Kato等[74]
    IoU均值 0.556 0.574 0.605 0.640 0.602
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-04-20
  • 录用日期:  2018-08-30
  • 刊出日期:  2019-04-20

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