Unsupervised depth estimation for ship target based on single view UAV image-Reference-Cited by-同舟云学术

Unsupervised depth estimation for ship target based on single view UAV image

Published:2022-05-03 Issue:9 Volume:43 Page:3216-3235
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Liu Tao¹,Jia Zi¹,Lei Zhengling²,Zhang Xiaocai³,Huo Yuchi⁴⁵

Affiliation:

1. College of Transport & Communications, Shanghai Maritime University, Shanghai, China

2. Engineering Science and Technology, Shanghai Ocean University, Shanghai, China

3. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

4. Smart Transportation Research Center, Zhejiang Lab, Hangzhou, China

5. State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Development of Science and Technology of Shanghai Ocean University

Hubei Key Laboratory of Inland Shipping Technology

Open Project Program of the State Key Lab of CAD&CG

State Key Laboratory of Engines

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2088260

Reference30 articles.

1. Bi3D: Stereo Depth Estimation via Binary Classifications

2. Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual Networks

3. Laplacian Pyramid Neural Network for Dense Continuous-Value Regression for Complex Scenes

4. Deep learning

5. Eigen, D., C. Puhrsch, and R. Fergus. 2014. ”Depth Map Prediction from a Single Image Using a Multi-Scale Deep Network.” arXiv preprint arXiv: 1406.2283.

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