Ordinal Depth Estimation

Ordinal Depth Estimation is a depth estimation framework that models depth as a relative ordinal relation between pixels or regions, differing from metric depth estimation by predicting the order of depth values rather than their exact continuous magnitudes.

15

Publications

2.4K

Citations

38

Authors

20

Institutions

Publications per year

2017–2026

5

Authors

38

  1. NS
    Noah Snavely

    Cornell University, United States

    2 publications · 1.2K citations

    2

    h-index

  2. ZL
    Zhengqi Li

    Cornell University, United States

    2 publications · 1.2K citations

    2

    h-index

  3. PI
    Phillip Isola

    Massachusetts Institute of Technology, United States

    2 publications · 238 citations

    2

    h-index

  4. XZ
    Xiaowei Zhou

    Zhejiang University, China

    1 publications · 442 citations

    1

    h-index

  5. KD
    Kostas Daniilidis

    University of Pennsylvania, United States

    1 publications · 442 citations

    1

    h-index

1–5 of 38

Venues

  1. Psychological Science

    2 publications · 96 citations

    2

    h-index

  2. 1

    h-index

  3. Ergonomics

    1 publications · 85 citations

    1

    h-index

  4. Psychological Review

    1 publications · 82 citations

    1

    h-index

  5. ACM Transactions on Graphics

    TOG · 1 publications · 73 citations

    1

    h-index

1–5