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Volume 20 Issue 6
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Zhenyu Li, Zehui Chen, Xianming Liu, Junjun Jiang. DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation. Machine Intelligence Research, vol. 20, no. 6, pp.837-854, 2023. https://doi.org/10.1007/s11633-023-1458-0
Citation: Zhenyu Li, Zehui Chen, Xianming Liu, Junjun Jiang. DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation. Machine Intelligence Research, vol. 20, no. 6, pp.837-854, 2023. https://doi.org/10.1007/s11633-023-1458-0
shu

DepthFormer: Exploiting Long-range Correlation and Local Information for Accurate Monocular Depth Estimation

doi: 10.1007/s11633-023-1458-0
  • 1.

    Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China

  • 2.

    Department of Automation, University of Science and Technology of China, Hefei 230026, China

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  • Author Bio:

    Zhenyu Li received the B. Sc. degree in computer science from Harbin Institute of Technology, China in 2021. He is a master student in computer science from Harbin Institute of Technology, China.His research interests include depth estimation and 3D object detection.E-mail: zhenyuli17@hit.edu.cnORCID iD: 0000-0003-2932-9179

    Zehui Chen received the B. Sc. degree in software engineering from Tongji University, China in 2020. He is currently a Ph. D. degree candidate with School of Information Science and Technology, University of Science and Technology of China (USTC), China.His research interests include object detection and multi-modal learning.E-mail: lovesnow@mail.ustc.edu.cn

    Xianming Liu received the B. Sc. M. Sc., and Ph. D. degrees in computer science from Harbin Institute of Technology (HIT), China in 2006, 2008 and 2012, respectively. In 2011, he spent half a year at Department of Electrical and Computer Engineering, McMaster University, Canada, as a visiting student, where he was a post-doctoral fellow from 2012 to 2013. He was a project researcher with National Institute of Informatics (NII), Japan from 2014 to 2017. He is currently a professor with School of Computer Science and Technology, HIT. He was a receipt of the IEEE ICME 2016 Best Student Paper Award.His research interests include object detection and multi-modal learning.E-mail: csxm@hit.edu.cn

    Junjun Jiang received the B. Sc. degree in mathematics from Huaqiao University, China in 2009, and the Ph. D. degree in computer science from Wuhan University, China in 2014. From 2015 to 2018, he was an associate professor with School of Computer Science, China University of Geosciences, China. From 2016 to 2018, he was a project researcher with National Institute of Informatics (NII), Japan. He is currently a professor with School of Computer Science and Technology, Harbin Institute of Technology, China. He won the Best Student Paper Runner-up Award at MMM 2017, the Finalist of the World′s FIRST 10K Best Paper Award at ICME 2017, and the Best Paper Award at IFTC 2018. He received the 2016 China Computer Federation (CCF) Outstanding Doctoral Dissertation Award and 2015 ACM Wuhan Doctoral Dissertation Award, China.His research interests include image processing and computer vision.E-mail: jiangjunjun@hit.edu.cn (Corresponding author)ORCID iD: 0000-0002-5694-505X

  • Received Date: 2023-03-05
  • Accepted Date: 2023-05-26
  • Publish Online: 2023-09-13
  • Publish Date: 2023-12-01
    Abstract
  • This paper aims to address the problem of supervised monocular depth estimation. We start with a meticulous pilot study to demonstrate that the long-range correlation is essential for accurate depth estimation. Moreover, the Transformer and convolution are good at long-range and close-range depth estimation, respectively. Therefore, we propose to adopt a parallel encoder architecture consisting of a Transformer branch and a convolution branch. The former can model global context with the effective attention mechanism and the latter aims to preserve the local information as the Transformer lacks the spatial inductive bias in modeling such contents. However, independent branches lead to a shortage of connections between features. To bridge this gap, we design a hierarchical aggregation and heterogeneous interaction module to enhance the Transformer features and model the affinity between the heterogeneous features in a set-to-set translation manner. Due to the unbearable memory cost introduced by the global attention on high-resolution feature maps, we adopt the deformable scheme to reduce the complexity. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets demonstrate that our proposed model, termed DepthFormer, surpasses state-of-the-art monocular depth estimation methods with prominent margins. The effectiveness of each proposed module is elaborately evaluated through meticulous and intensive ablation studies.

     

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