LocalBins: Improving Depth Estimation by Learning Local Distributions-Reference-Cited by-同舟云学术

LocalBins: Improving Depth Estimation by Learning Local Distributions

Published:2022 Issue: Volume: Page:480-496
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Bhat Shariq Farooq^ORCID,Alhashim Ibraheem^ORCID,Wonka Peter^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19769-7_28

Reference41 articles.

1. Akada, H., Bhat, S.F., Alhashim, I., Wonka, P.: Self-supervised learning of domain invariant features for depth estimation. In: IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Waikoloa, HI, USA, 3–8 January 2022, pp. 997–1007. IEEE (2022). https://doi.org/10.1109/WACV51458.2022.00107

2. Alhashim, I., Wonka, P.: High quality monocular depth estimation via transfer learning. CoRR abs/1812.11941 (2018). http://arxiv.org/abs/1812.11941

3. Atapour-Abarghouei, A., Breckon, T.P.: Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2800–2810 (2018)

4. Bhat, S.F., Alhashim, I., Wonka, P.: AdaBins: depth estimation using adaptive bins. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4008–4017. IEEE Computer Society, Los Alamitos, CA, USA, June 2021. https://doi.org/10.1109/CVPR46437.2021.00400

5. Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Unsupervised monocular depth and ego-motion learning with structure and semantics. In: CVPR Workshop on Visual Odometry and Computer Vision Applications Based on Location Cues (VOCVALC) (2019)

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