CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-12-29
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Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-1802-8197
https://orcid.org/0000-0001-7722-7172
https://orcid.org/0000-0002-3045-624X
https://orcid.org/0000-0002-8043-0312
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin. Deep 3D reconstruction: methods, data, and challenges[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(5): 652-672.
@article{title="Deep 3D reconstruction: methods, data, and challenges",
author="Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="5",
pages="652-672",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000068"
}
%0 Journal Article
%T Deep 3D reconstruction: methods, data, and challenges
%A Caixia Liu
%A Dehui Kong
%A Shaofan Wang
%A Zhiyong Wang
%A Jinghua Li
%A Baocai Yin
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 5
%P 652-672
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000068
TY - JOUR
T1 - Deep 3D reconstruction: methods, data, and challenges
A1 - Caixia Liu
A1 - Dehui Kong
A1 - Shaofan Wang
A1 - Zhiyong Wang
A1 - Jinghua Li
A1 - Baocai Yin
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 5
SP - 652
EP - 672
%@ 2095-9184
Y1 - 2021
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 can lead to cumulative errors, but can hardly learn semantic features of 3D shapes automatically; (2) they depend heavily on the content and 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 network based methods, and analyze the corresponding methodologies carefully. Second, we investigate four representative databases that are commonly used by the above methods in detail. Third, we give a comprehensive comparison of 3D reconstruction methods based on deep learning, which consists of the 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 views. Finally, we discuss future development of 3D reconstruction methods based on deep learning.
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