Full Text:   <176>

CLC number: 

On-line Access: 2020-04-17

Received: 2020-02-11

Revision Accepted: 2020-03-23

Crosschecked: 0000-00-00

Cited: 0

Clicked: 312

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000068


Deep 3D reconstruction: methods, data, and challenges


Author(s):  Cai-xia LIU, De-hui KONG, Shao-fan WANG, Zhi-yong WANG, Jing-hua LI, Bao-cai YIN

Affiliation(s):  Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Artificial Intelligence Institute, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; more

Corresponding email(s):   lcxxib@emails.bjut.edu.cn, wangshaofan@bjut.edu.cn

Key Words:  Deep learning models, Three-dimensional reconstruction, Recurrent neural network, Deep AutoEncoder, Generative adversarial network, Convolutional neural network


Cai-xia LIU, De-hui KONG, Shao-fan WANG, Zhi-yong WANG, Jing-hua LI, Bao-cai YIN. Deep 3D reconstruction: methods, data, and challenges[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

@article{title="Deep 3D reconstruction: methods, data, and challenges",
author="Cai-xia LIU, De-hui KONG, Shao-fan WANG, Zhi-yong WANG, Jing-hua LI, Bao-cai YIN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000068"
}

%0 Journal Article
%T Deep 3D reconstruction: methods, data, and challenges
%A Cai-xia LIU
%A De-hui KONG
%A Shao-fan WANG
%A Zhi-yong WANG
%A Jing-hua LI
%A Bao-cai YIN
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000068

TY - JOUR
T1 - Deep 3D reconstruction: methods, data, and challenges
A1 - Cai-xia LIU
A1 - De-hui KONG
A1 - Shao-fan WANG
A1 - Zhi-yong WANG
A1 - Jing-hua LI
A1 - Bao-cai YIN
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000068


Abstract: 
Three-dimensional (3D) reconstruction of shapes is an important research topic in the fields of computer vision, computer graphics, pattern recognition, and virtual reality. Existing 3D reconstruction methods usually suffer from two bottlenecks: (1) they involve multiple manually designed states, which lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they heavily depend on the content and the quality of images, as well as precisely calibrated cameras. As a result, it is difficult to improve the reconstruction accuracy of those methods. 3D reconstruction methods based on deep learning overcome both of these bottlenecks by automatically learning semantic features of 3D shapes from low-quality images using deep networks. However, while these methods have various architectures, in-depth analysis and comparisons of them are unavailable so far. We present a comprehensive survey of 3D reconstruction methods based on deep learning. First, based on different deep learning model architectures, we divide 3D reconstruction methods based on deep learning into four types: recurrent neural network-, deep AutoEncoder-, generative adversarial network-, and convolutional neural networkbased methods, and analyze the corresponding methodologies carefully. Second, we investigate, in detail, four representative databases that are commonly used by the above methods in details. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of results of different methods with respect to the same database, the results of each method with respect to different databases, and the robustness of each method with respect to the number of viewpoints. Finally, we discuss future development of 3D reconstruction methods based on deep learning.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE