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Depth Prediction

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    The Edge of Depth: Explicit Constraints between Segmentation and Depth

    Shengjie Zhu, Garrick Brazil, Xiaoming Liu

    In this work we study the mutual benefits of two common computer vision tasks, self-supervised depth estimation and semantic segmentation from images. For example, to help unsupervised monocular depth estimation, constraints from semantic segmentation has been explored implicitly such as sharing and transforming features. In contrast, we propose to explicitly ...

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    Keywords: Depth Prediction

2020

  • The Edge of Depth: Explicit Constraints between Segmentation and Depth
    Shengjie Zhu, Garrick Brazil, Xiaoming Liu
    In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, Jun. 2020
    Bibtex | PDF | arXiv | Supplemental | Project Webpage | Video
  • @inproceedings{ the-edge-of-depth-explicit-constraints-between-segmentation-and-depth,
      author = { Shengjie Zhu and Garrick Brazil and Xiaoming Liu },
      title = { The Edge of Depth: Explicit Constraints between Segmentation and Depth },
      booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
      address = { Seattle, WA },
      month = { June },
      year = { 2020 },
    }