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Publication Detail
Self-supervised monocular depth hints
  • Publication Type:
    Conference
  • Authors:
    Watson J, Firman M, Brostow G, Turmukhambetov D
  • Publisher:
    IEEE
  • Publication date:
    27/02/2020
  • Pagination:
    2162, 2171
  • Published proceedings:
    Proceedings of the IEEE International Conference on Computer Vision
  • Volume:
    2019-October
  • ISBN-13:
    9781728148038
  • Status:
    Published
  • Name of conference:
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Conference place:
    Seoul, Korea (South)
  • Conference start date:
    27/10/2019
  • Conference finish date:
    02/11/2019
  • Print ISSN:
    1550-5499
Abstract
© 2019 IEEE. Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser-scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground-truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth-prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth-suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.
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