CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss-Reference-Cited by-同舟云学术

CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss

Published:2020 Issue: Volume: Page:316-331
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
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Author:

Wang Lijun^ORCID,Zhang Jianming^ORCID,Wang Yifan^ORCID,Lu Huchuan^ORCID,Ruan Xiang^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58558-7_19

Reference35 articles.

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2. Chen, W., Fu, Z., Yang, D., Deng, J.: Single-image depth perception in the wild. In: NIPS, pp. 730–738 (2016)

3. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213–3223 (2016)

4. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)

5. Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: ICCV, pp. 2650–2658 (2015)

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