Due to the significant non-linear distortions of fisheye camera models, supervised learning techniques for monocular depth estimation usually do not generalize well when applied to this kind of projection models. Additionally, there is no publicly available dataset for monocular fisheye camera depth estimation emphasizing automated valet parking. As a result, we initiate a unique depth estimate fisheye dataset optimized for automated valet parking. On this premise, we also construct a system for monocular fisheye depth estimation using an enhanced deep optimizer for improving the results of supervised monocular depth estimation on fisheye camera images. The proposed method produces impressive results on our ZMDepth dataset. We also evaluate our optimizer-based method on the KITTI dataset and achieved state-of-the-art results. We will make a partial ZMDepth dataset publicly available at: https://github.com/NamespaceMain/ZMDepth.


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    Titel :

    Monocular Fisheye Depth Estimation for Automated Valet Parking: Dataset, Baseline and Deep Optimizers


    Beteiligte:
    Wu, Zizhang (Autor:in) / Fan, Zhi-Gang (Autor:in) / Li, Zhuozheng (Autor:in) / Wang, Jizheng (Autor:in) / Xu, Tianhao (Autor:in) / Tang, Qiang (Autor:in) / Wang, Fan (Autor:in) / Luo, Zhengbo (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    2524558 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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