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OptiDepthNet: A Real-Time Unsupervised Monocular Depth Estimation Network

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Abstract

With the development of deep learning, the network architectures and algorithm accuracy applied to monocular depth estimation have been greatly improved. However, these complex network structures can be very difficult to realize real-time processing on embedded platforms. Consequently, this study proposed a lightweight encoding and decoding structure based on the U-Net model. The depthwise separable convolution was introduced into the encoder and decoder to optimize the network structure, further reduce the computational complexity, and improve the running speed, the implementation algorithm being more suitable for embedded platforms. When the accuracy of similar depth images was achieved, the network parameters could be reduced by up to eight times, and the running speed could be more than doubled. The research showed the proposed method to be very effective, having a certain reference value in monocular depth estimation algorithms running on embedded platforms.

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Acknowledgements

Thanks to Godard and his team who shared their results.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC Grant No. 61903124).

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Authors and Affiliations

  1. School of Computer and Information, Hohai University, Nanjing, 211100, China

    Feng Wei, XingHui Yin, Jie Shen & HuiBin Wang

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  1. Feng Wei
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  2. XingHui Yin
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Correspondence to HuiBin Wang.

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Wei, F., Yin, X., Shen, J. et al. OptiDepthNet: A Real-Time Unsupervised Monocular Depth Estimation Network. Wireless Pers Commun 128, 2831–2846 (2023). https://doi.org/10.1007/s11277-022-10074-9

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