Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2022-11-01


    Format / Umfang :

    4866531 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Improved Convolutional Neural Network for Monocular Depth Estimation

    Kang, Jing / Dang, Anrong / Zhang, Bailing et al. | Springer Verlag | 2020


    Monocular depth estimation

    Europäisches Patentamt | 2021

    Freier Zugriff

    Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks

    Mern, John M. / Julian, Kyle D. / Tompa, Rachael E. et al. | TIBKAT | 2019


    Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks

    Mern, John M. / Julian, Kyle D. / Tompa, Rachael E. et al. | AIAA | 2019


    Monocular Depth Estimation Using Information Exchange Network

    Su, Wen / Zhang, Haifeng / Zhou, Quan et al. | IEEE | 2021