ShanghaiTech University Knowledge Management System
| Multi-Dimensional Residual Dense Attention Network for Stereo Matching | |
| 2019 | |
| Source Publication | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| Volume | 7Issue:99Pages:51681-51690 |
| Status | 已发表 |
| DOI | 10.1109/ACCESS.2019.2911618 |
| Abstract | Very 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. |
| Keyword | Stereo matching multi-dimensional residual dense attention hierarchical 3D attention mechanism |
| URL | 查看原文 |
| Indexed By | SCI ; SCIE ; EI |
| Language | 英语 |
| Funding Project | Shanghai Science and Technology Committee, China[17511108202] |
| WOS Research Area | Computer Science ; Engineering ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS ID | WOS:000466706300001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| EI Accession Number | 20192407025701 |
| EI Keywords | Convolution ; Deep neural networks ; Extraction ; Feature extraction ; Neural networks ; Semantics ; Textures |
| EI Classification Number | Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Chemical Operations:802.3 |
| Original Document Type | Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://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 Author | Li, Jiamao |
| Affiliation | 1.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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