Channel Attentive Self-supervised Network for Monocular Depth Estimation

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.