Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (02): 22-29.doi: 10.12052/gdutxb.210139

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Channel Attentive Self-supervised Network for Monocular Depth Estimation

Wu Jun-xian, He Yuan-lie   

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-09-17 Online:2023-03-25 Published:2023-04-07

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Abstract: A new method is proposed for self-supervised monocular depth estimation. Although recent methods have been able to produce high-precision depth maps, previous work has ignored channel-wise information in the image. To solve this problem, Channel Attention is introduced and improvements made in two aspects of the network structure: (a) a Squeeze-and-Excitation (SE) block is injected into the corresponding networks to capture the channel relation in feature map; (b) a Channel Attention Dense Connection (CADC) block is applied to combine multi-scale features and recalibrate channel-wise feature. Experiments on the KITTI dataset show the effectiveness of the proposed approach, which outperforms quantitatively and qualitatively the state-of-the-art Self-Supervised Depth Estimation methods.

Key words: monocular depth estimation, attention mechanism, self-supervised deep-learning

CLC Number: 

  • TP249
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