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  • Uncertainty estimation for stereo matching based on evidential deep learning

    Author(s)
    Wang, Chen
    Wang, Xiang
    Zhang, Jiawei
    Zhang, Liang
    Bai, Xiao
    Ning, Xin
    Zhou, Jun
    Hancock, Edwin
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2022
    Metadata
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    Abstract
    Although deep learning-based stereo matching approaches have achieved excellent performance in recent years, it is still a non-trivial task to estimate the uncertainty of the produced disparity map. In this paper, we propose a novel approach to estimate both aleatoric and epistemic uncertainties for stereo matching in an end-to-end way. We introduce an evidential distribution, named Normal Inverse-Gamma (NIG) distribution, whose parameters can be used to calculate the uncertainty. Instead of directly regressed from aggregated features, the uncertainty parameters are predicted for each potential disparity and then averaged ...
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    Although deep learning-based stereo matching approaches have achieved excellent performance in recent years, it is still a non-trivial task to estimate the uncertainty of the produced disparity map. In this paper, we propose a novel approach to estimate both aleatoric and epistemic uncertainties for stereo matching in an end-to-end way. We introduce an evidential distribution, named Normal Inverse-Gamma (NIG) distribution, whose parameters can be used to calculate the uncertainty. Instead of directly regressed from aggregated features, the uncertainty parameters are predicted for each potential disparity and then averaged via the guidance of matching probability distribution. Furthermore, considering the sparsity of ground truth in real scene datasets, we design two additional losses. The first one tries to enlarge uncertainty on incorrect predictions, so uncertainty becomes more sensitive to erroneous regions. The second one enforces the smoothness of the uncertainty in the regions with smooth disparity. Most stereo matching models, such as PSM-Net, GA-Net, and AA-Net, can be easily integrated with our approach. Experiments on multiple benchmark datasets show that our method improves stereo matching results. We prove that both aleatoric and epistemic uncertainties are well-calibrated with incorrect predictions. Particularly, our method can capture increased epistemic uncertainty on out-of-distribution data, making it effective to prevent a system from potential fatal consequences. Code is available at https://github.com/Dawnstar8411/StereoMatching-Uncertainty.
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    Journal Title
    Pattern Recognition
    Volume
    124
    DOI
    https://doi.org/10.1016/j.patcog.2021.108498
    Note
    This publication has been entered as an advanced online version in Griffith Research Online.
    Subject
    Information systems
    Science & Technology
    Computer Science, Artificial Intelligence
    Engineering, Electrical & Electronic
    Publication URI
    http://hdl.handle.net/10072/411549
    Collection
    • Journal articles

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