Affordance Detection

Affordance Detection is a computational perception framework that identifies actionable possibilities offered by objects or environments in a scene by integrating sensory inputs with semantic and geometric reasoning to determine how an agent can physically interact with them.

113

Publications

6.6K

Citations

308

Authors

140

Institutions

Publications per year

2017–2026

70

Authors

308

  1. YC
    Yang Cao

    University of Science and Technology of China, China

    7 publications · 208 citations

    7

    h-index

  2. WZ
    Wei Zhai

    University of Science and Technology of China, China

    6 publications · 174 citations

    6

    h-index

  3. HL
    Hongchen Luo

    University of Science and Technology of China, China

    6 publications · 174 citations

    6

    h-index

  4. JZ
    Jing Zhang

    The University of Sydney, Australia

    5 publications · 173 citations

    5

    h-index

  5. DT
    Dacheng Tao

    The University of Sydney, Australia

    5 publications · 244 citations

    5

    h-index

1–5 of 308

Venues

  1. IEEE Robotics and Automation Letters

    7 publications · 256 citations

    7

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 143 citations

    6

    h-index

  3. 4

    h-index

  4. Neuropsychologia

    2 publications · 602 citations

    2

    h-index

  5. 2

    h-index

1–5