Lightweight Self-supervised Monocular Depth Estimation

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    Currently, most augmented reality and autonomous driving applications use not only the depth information estimated by the depth network but also the pose information estimated by the pose network. Integrating both the pose network and the depth network into an embedded device can be extremely memory-consuming. In view of this problem, a method of the depth and pose networks sharing feature extractors is proposed to keep the model at a lightweight size. In addition, the depth-separable convolutional lightweight depth network with linear structure allows the network to obtain fewer parameters without losing too much detailed information. Finally, experiments on the KITTI dataset show that compared with the algorithms of the same type, the size of the pose and deep network parameters is only 35.33 MB. At the same time, the average absolute error of the restored depth map is also maintained at 0.129.

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  • Received:February 03,2023
  • Revised:March 01,2023
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  • Online: May 19,2023
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