Multi-Dimensional Residual Dense Attention Network for Stereo Matching
Zhang, Guanghui1,2; Zhu, Dongchen1; Shi, Wenjun1,2; Ye, Xiaoqing1,2; Li, Jiamao1,2; Zhang, Xiaolin1,2,3
2019
Source PublicationIEEE ACCESS
ISSN2169-3536
Volume7Issue:99Pages:51681-51690
Status已发表
DOI10.1109/ACCESS.2019.2911618
AbstractVery deep convolutional neural networks (CNNs) have recently achieved great success in stereo matching. It is still highly desirable to learn a robust feature map to improve ill-posed regions, such as weakly textured regions, reflective surfaces, and repetitive patterns. Therefore, we propose an endto-end multi-dimensional residual dense attention network (MRDA-Net) in this paper, focusing on more comprehensive pixel-wise feature extraction. Our proposed network consists of two parts: the 2D residual dense attention net for feature extraction and the 3D convolutional attention net for matching. Our designed 2D residual dense attention net uses a dense network structure to fully exploit the hierarchical features from preceding convolutional layers and uses residual network structure to fuse low-level structure information and high-level semantic information. The 2D attention module of the net aims to adaptively recalibrate channel-wise features to be more concerned about informative features. Our proposed 3D convolutional attention net further expands attention mechanism for matching. The stacked hourglass module of the net is exploited to extract multi-scale context information as well as geometry information. The novel 3D attention module of the net aggregates hierarchical sub-cost volumes adaptively instead of manually and then achieves a comprehensive recalibrated cost volume for more correct disparity computation. The experiments demonstrate that our approach achieves the state-of-the-art accuracy on Scene Flow dataset and KITTI 2012 and KITTI 2015 Stereo datasets.
KeywordStereo matching multi-dimensional residual dense attention hierarchical 3D attention mechanism
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Indexed BySCI ; EI
Language英语
Funding ProjectShanghai Science and Technology Committee, China[17511108202]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000466706300001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20192407025701
EI KeywordsConvolution ; Deep neural networks ; Extraction ; Feature extraction ; Neural networks ; Semantics ; Textures
EI Classification NumberInformation Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Chemical Operations:802.3
Original Document TypeArticle
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Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/31163
Collection信息科学与技术学院_特聘教授组_张晓林组
Corresponding AuthorLi, Jiamao
Affiliation1.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Bion Vis Syst Lab, State Key Lab Transducer Technol, Shanghai 200050, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
3.ShanghaiTech Univ, Sch Informat & Technol, Shanghai 200050, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Guanghui,Zhu, Dongchen,Shi, Wenjun,et al. Multi-Dimensional Residual Dense Attention Network for Stereo Matching[J]. IEEE ACCESS,2019,7(99):51681-51690.
APA Zhang, Guanghui,Zhu, Dongchen,Shi, Wenjun,Ye, Xiaoqing,Li, Jiamao,&Zhang, Xiaolin.(2019).Multi-Dimensional Residual Dense Attention Network for Stereo Matching.IEEE ACCESS,7(99),51681-51690.
MLA Zhang, Guanghui,et al."Multi-Dimensional Residual Dense Attention Network for Stereo Matching".IEEE ACCESS 7.99(2019):51681-51690.
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