Self-supervised monocular depth estimation based on image texture detail enhancement-Reference-Cited by-同舟云学术

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Self-supervised monocular depth estimation based on image texture detail enhancement

Published:2021-06-25 Issue:9-11 Volume:37 Page:2567-2580
ISSN:0178-2789
Container-title:The Visual Computer
language:en
Short-container-title:Vis Comput

Author:

Li Yuanzhen,Luo Fei,Li Wenjie,Zheng Shenjie,Wu Huan-huan,Xiao Chunxia

Funder

the Key Technological Innovation Projects of Hubei Province

Wuhan University-Huawei GeoInformatices Innovation Lab

National Natural Science Foundation of China

Science and Technology Cooperation Project of The Xinjiang Production and Construction Corps

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s00371-021-02206-2.pdf

Reference66 articles.

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2. Burt, P., Adelson, E.: The laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)

3. Casser, V., Pirk, S., Mahjourian, R., Angelova, A.: Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In: AAAI, pp. 8001–8008 (2019)

4. Chen, P.Y., Liu, A.H., Liu, Y.C., Wang, Y.C.F.: Towards scene understanding: Unsupervised monocular depth estimation with semantic-aware representation. In: CVPR, pp. 2619–2627 (2019)

5. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213–3223 (2016)

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