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    AdaBins: Depth Estimation using Adaptive Bins

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    Preprintfile1.pdf
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    Pre-print
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    Type
    Preprint
    Authors
    Bhat, Shariq Farooq
    Alhashim, Ibraheem cc
    Wonka, Peter cc
    KAUST Department
    KAUST
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2020-11-28
    Permanent link to this record
    http://hdl.handle.net/10754/666232
    
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    Abstract
    We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network architecture and pose the question of how the global processing of information can help improve overall depth estimation. To this end, we propose a transformer-based architecture block that divides the depth range into bins whose center value is estimated adaptively per image. The final depth values are estimated as linear combinations of the bin centers. We call our new building block AdaBins. Our results show a decisive improvement over the state-of-the-art on several popular depth datasets across all metrics. We also validate the effectiveness of the proposed block with an ablation study and provide the code and corresponding pre-trained weights of the new state-of-the-art model.
    Publisher
    arXiv
    arXiv
    2011.14141
    Additional Links
    https://arxiv.org/pdf/2011.14141
    Collections
    Preprints; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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