Learning-based Stereo Matching

Learning-Based Stereo Matching is a stereo matching method that employs machine learning models to infer pixel correspondences between image pairs by learning matching priors from data, distinguishing it from traditional geometry-driven or hand-crafted cost-based approaches.

66

Publications

3.7K

Citations

229

Authors

86

Institutions

Publications per year

2017–2026

53

Authors

229

  1. ZR
    Zhibo Rao

    Northwestern Polytechnical University, China

    4 publications · 132 citations

    4

    h-index

  2. PM
    Philippos Mordohai

    Stevens Institute of Technology, United States

    4 publications · 196 citations

    4

    h-index

  3. JZ
    Jingyang Zhang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 87 citations

    3

    h-index

  4. ZL
    Zixin Luo

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 87 citations

    3

    h-index

  5. YY
    Yao Yao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 87 citations

    3

    h-index

1–5 of 229

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 210 citations

    8

    h-index

  2. 4

    h-index

  3. Pattern Recognition

    4 publications · 295 citations

    4

    h-index

  4. International Journal of Computer Vision

    3 publications · 240 citations

    3

    h-index

  5. IEEE Robotics and Automation Letters

    2 publications · 100 citations

    2

    h-index

1–5