Journal of Guangdong University of Technology ›› 2020, Vol. 37 ›› Issue (04): 35-41.doi: 10.12052/gdutxb.190140

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A Monocular Depth Estimation Combined with Attention and Unsupervised Deep Learning

Cen Shi-jie, He Yuan-lie, Chen Xiao-cong   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2019-11-18 Online:2020-07-11 Published:2020-07-02

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Abstract: To solve the problem of boundary blurring of current unsupervised monocular depth estimation method, a network architecture is proposed based on dual attention module. This architecture can effectively solve the problem of boundary blurring of depth estimation by using long-range context information of image features. The model framework that includes depth estimation network and pose estimation network is trained by an unsupervised method based on view synthesis and estimation depth and camera pose transformation at the same time. The dual attention module is embedded in the depth estimation network, including position attention module and channel attention module. This module can represent the long-range spatial location and the context information between different feature maps, so that the network can estimate the depth information with better details. The experimental results on the KITTI dataset and the Make3D dataset show that our method can effectively improve the accuracy of the monocular depth estimation and can solve the depth estimation boundary blur problem.

Key words: depth estimation, unsupervised learning, deep learning, attention, robotics

CLC Number: 

  • TP249
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