Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Learning adversarial point-wise domain alignment for stereo matching | |
Zhang, Chenghao1,2; Meng, Gaofeng1,2,3 ; Xu, Richard Yi Da4; Xiang, Shiming1,2; Pan, Chunhong1
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| Source Publication | NEUROCOMPUTING
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| ISSN | 0925-2312 |
| 2022-06-28 | |
| Volume | 491Pages:564-574 |
| Corresponding Author | Meng, Gaofeng(gfmeng@nlpr.ia.ac.cn) |
| Abstract | The state-of-the-art stereo matching models trained on synthetic datasets have difficulty in generalizing to real-world datasets. One major reason is that illumination and texture in the real world are hard to be simulated, resulting in big differences between synthetic and real-world data. In this study, instead of narrowing the image-level appearance difference, we focus on aligning both data domains in feature space in an unsupervised manner and propose an end-to-end domain alignment stereo network (DAStereo). A domain alignment module (DAM) is introduced by learning a point-wise linear transformation. We demonstrate that DAM can maintain sufficient alignment capacity with fewer parameters than the globally nonlinear mapping. To explicitly promote the point-wise domain alignment, adversarial learning is further introduced using a cost volume discriminator in a hybrid training manner. Experimental results show that DAStereo outperforms the state-of-the-art unsupervised and adaptive methods and even achieves comparable performance to some supervised methods. (C) 2021 Elsevier B.V. All rights reserved. |
| Keyword | Stereo Matching Domain adaptation Point-wise linear transformation Adversarial learning |
| DOI | 10.1016/j.neucom.2021.12.034 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2018AAA0100400] ; National Natural Science Foundation of China[61802407] ; National Natural Science Foundation of China[61976208] ; National Natural Science Foundation of China[62071466] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000830181200013 |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49843 |
| Collection | 模式识别国家重点实验室_先进时空数据分析与学习 |
| Corresponding Author | Meng, Gaofeng |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Artificial Intelligence & Robot, HK Inst Sci & Innovat, Beijing, Peoples R China 4.Univ Technol Sydney UTS, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Zhang, Chenghao,Meng, Gaofeng,Xu, Richard Yi Da,et al. Learning adversarial point-wise domain alignment for stereo matching[J]. NEUROCOMPUTING,2022,491:564-574. |
| APA | Zhang, Chenghao,Meng, Gaofeng,Xu, Richard Yi Da,Xiang, Shiming,&Pan, Chunhong.(2022).Learning adversarial point-wise domain alignment for stereo matching.NEUROCOMPUTING,491,564-574. |
| MLA | Zhang, Chenghao,et al."Learning adversarial point-wise domain alignment for stereo matching".NEUROCOMPUTING 491(2022):564-574. |
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