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Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation

文献类型：期刊论文

作者Zhang, Yuyang1,2; Xu, Shibiao1,2; Wu, Baoyuan3; Shi, Jian1,2; Meng, Weiliang1,2; Zhang, Xiaopeng1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2020
卷号29页码:7019-7031
ISSN号1057-7149
关键词Estimation Training Feature extraction Geometry Computer vision Cameras Unsupervised learning Unsupervised learning DenseDepthNet multi-view geometry constraint depth consistency
DOI10.1109/TIP.2020.2997247
通讯作者Xu, Shibiao(shibiao.xu@nlpr.ia.ac.cn) ; Meng, Weiliang(weiliang.meng@ia.ac.cn)
英文摘要Accurate 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.
资助项目National 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]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000546910100009
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/40092]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Xu, Shibiao; Meng, Weiliang
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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