Review of Stereo Matching Algorithms Based on Deep Learning
Joint Authors
Zhou, Kun
Meng, Xiangxi
Cheng, Bo
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-23
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Stereo vision is a flourishing field, attracting the attention of many researchers.
Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches.
This review presents an overview of different stereo matching algorithms based on deep learning.
For convenience, we classified the algorithms into three categories: (1) non-end-to-end learning algorithms, (2) end-to-end learning algorithms, and (3) unsupervised learning algorithms.
We have provided a comprehensive coverage of the remarkable approaches in each category and summarized the strengths, weaknesses, and major challenges, respectively.
The speed, accuracy, and time consumption were adopted to compare the different algorithms.
American Psychological Association (APA)
Zhou, Kun& Meng, Xiangxi& Cheng, Bo. 2020. Review of Stereo Matching Algorithms Based on Deep Learning. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138839
Modern Language Association (MLA)
Zhou, Kun…[et al.]. Review of Stereo Matching Algorithms Based on Deep Learning. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1138839
American Medical Association (AMA)
Zhou, Kun& Meng, Xiangxi& Cheng, Bo. Review of Stereo Matching Algorithms Based on Deep Learning. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1138839
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138839