CASIA OpenIR  > 多模态人工智能系统全国重点实验室  > 三维可视计算
Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation
Zhang, Yuyang1,2; Xu, Shibiao1,2; Wu, Baoyuan3; Shi, Jian1,2; Meng, Weiliang1,2; Zhang, Xiaopeng1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2020
Volume29Pages:7019-7031
Corresponding AuthorXu, Shibiao(shibiao.xu@nlpr.ia.ac.cn) ; Meng, Weiliang(weiliang.meng@ia.ac.cn)
AbstractAccurate depth estimation from images is a fundamental problem in computer vision. In this paper, we propose an unsupervised learning based method to predict high-quality depth map from multiple images. A novel multi-view constrained DenseDepthNet is designed for this task. Our DenseDepthNet can effectively leverage both the low-level and high-level features of input images and generate appealing results, especially with sharp details. We employ the public datasets KITTI and Cityscapes for training in an end-to-end unsupervised fashion. A novel depth consistency loss based on multi-view geometry constraint is also applied to the corresponding points across pairwise images, which helps to improve the quality of predicted depth maps significantly. We conduct comprehensive evaluations on our DenseDepthNet and our depth consistency loss function. Experiments validate that our method outperforms the state-of-the-art unsupervised methods and produce comparable results with supervised methods.
KeywordEstimation Training Feature extraction Geometry Computer vision Cameras Unsupervised learning Unsupervised learning DenseDepthNet multi-view geometry constraint depth consistency
DOI10.1109/TIP.2020.2997247
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018YFB2100601] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[61771026] ; National Natural Science Foundation of China[61972459] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[61571046] ; National Natural Science Foundation of China[61561003]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000546910100009
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification三维视觉
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40093
Collection多模态人工智能系统全国重点实验室_三维可视计算
Corresponding AuthorXu, Shibiao; Meng, Weiliang
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Univ Hong Kong, Shenzhen 518172, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yuyang,Xu, Shibiao,Wu, Baoyuan,et al. Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:7019-7031.
APA Zhang, Yuyang,Xu, Shibiao,Wu, Baoyuan,Shi, Jian,Meng, Weiliang,&Zhang, Xiaopeng.(2020).Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,7019-7031.
MLA Zhang, Yuyang,et al."Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):7019-7031.
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