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Unsupervised Learning of Depth, Optical Flow and Pose With Occlusion From 3D Geometry

  • Published:2022-01 Issue:1 Volume:23 Page:308-320
  • ISSN:1524-9050
  • Container-title:IEEE Transactions on Intelligent Transportation Systems
  • language:
  • Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wang GuangmingORCID,Zhang Chi,Wang HeshengORCID,Wang JingchuanORCID,Wang Yong,Wang Xinlei

Funder

Natural Science Foundation of China

Beijing Advanced Innovation Center for Intelligent Robots and Systems

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Cited by 40 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey;ACM Computing Surveys;2024-07-15

2. Timeliness-Fidelity Tradeoff in 3D Scene Representations;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2024-05-20

3. Self-Supervised Multi-Scale Hierarchical Refinement Method for Joint Learning of Optical Flow and Depth;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Joint estimation of pose, depth, and optical flow with a competition–cooperation transformer network;Neural Networks;2024-03

5. Joint self-supervised learning of interest point, descriptor, depth, and ego-motion from monocular video;Multimedia Tools and Applications;2024-02-26
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