Transparent Object Perception

Transparent Object Perception is a computational vision framework that enables machines to detect, segment, and interpret transparent objects in three-dimensional scenes by modeling their unique light transmission, refraction, and boundary properties, distinguishing it from general object perception methods that typically assume opaque surface reflectance.

49

Publications

2.3K

Citations

114

Authors

63

Institutions

Publications per year

2017–2026

11

Authors

114

  1. FF
    Franz Faul

    Christian-Albrechts-Universität zu Kiel, Germany

    3 publications · 44 citations

    3

    h-index

  2. MI
    Masanori Idesawa

    RIKEN, Japan

    2 publications · 30 citations

    2

    h-index

  3. DW
    Daphna Weinshall

    Hebrew University of Jerusalem, Israel

    2 publications · 65 citations

    2

    h-index

  4. ZZ
    Zheming Zhou

    University of Michigan, United States

    2 publications · 37 citations

    2

    h-index

  5. NS
    Nick Schlüter

    Christian-Albrechts-Universität zu Kiel, Germany

    2 publications · 33 citations

    2

    h-index

1–5 of 114

Venues

  1. Journal of Vision

    5 publications · 160 citations

    5

    h-index

  2. Perception

    5 publications · 286 citations

    5

    h-index

  3. Vision Research

    3 publications · 79 citations

    3

    h-index

  4. Japanese Journal of Applied Physics

    JJAP · 2 publications · 30 citations

    2

    h-index

  5. Perception & Psychophysics

    2 publications · 201 citations

    2

    h-index

1–5