Watson, J;
Firman, M;
Brostow, G;
Turmukhambetov, D;
(2020)
Self-supervised monocular depth hints.
In:
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
(pp. pp. 2162-2171).
The Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Self-supervised monocular depth hints |
| Event: | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) |
| ISBN-13: | 978-1-7281-4803-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICCV.2019.00225 |
| Publisher version: | https://doi.org/10.1109/ICCV.2019.00225 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Training, Cameras, Videos, Estimation, Prediction algorithms, Image color analysis, Laser radar |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117262 |
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