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Digging Into Self-Supervised Monocular Depth Estimation

Godard, Clément and Mac Aodha, Oisin and Firman, Michael and Brostow, Gabriel (2019) Digging Into Self-Supervised Monocular Depth Estimation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE , Piscataway, NJ, pp. 3827-3837. ISBN 9781728148038.

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Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark.

Item Type:Book Section
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Mac Aodha, Oisin0000-0002-5787-5073
Additional Information:© 2019 IEEE. Thanks to the authors who shared their results, and Peter Hedman, Daniyar Turmukhambetov, and Aron Monszpart for their helpful discussions.
Record Number:CaltechAUTHORS:20200306-092558467
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Official Citation:C. Godard, O. M. Aodha, M. Firman and G. Brostow, "Digging Into Self-Supervised Monocular Depth Estimation," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 3827-3837. doi: 10.1109/ICCV.2019.00393
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101736
Deposited By: Tony Diaz
Deposited On:06 Mar 2020 17:43
Last Modified:16 Nov 2021 18:05

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