Point Cloud Grasp Detection

Point Cloud Grasp Detection is a grasp detection framework that operates directly on three-dimensional point cloud data to identify optimal grasp poses for robotic manipulation, distinguished from image-based or voxel-based approaches by its reliance on unstructured, sparse geometric representations of object surfaces.

39

Publications

3.3K

Citations

134

Authors

46

Institutions

Publications per year

2017–2026

26

Authors

134

  1. RW
    Robert W. Platt

    McGill University, Canada

    3 publications · 142 citations

    3

    h-index

  2. AT
    Andreas ten Pas

    Northeastern University, United States

    3 publications · 142 citations

    3

    h-index

  3. MV
    Markus Vincze

    TU Wien, Austria

    3 publications · 138 citations

    3

    h-index

  4. PG
    Pablo Gil

    University of Alicante, Spain

    2 publications · 88 citations

    2

    h-index

  5. BS
    Brayan S. Zapata-Impata

    University of Alicante, Spain

    2 publications · 88 citations

    2

    h-index

1–5 of 134

Venues

  1. The International Journal of Robotics Research

    IJRR · 3 publications · 1K citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 59 citations

    2

    h-index

  3. Pattern Recognition

    2 publications · 106 citations

    2

    h-index

  4. IEEE Robotics and Automation Letters

    3 publications · 73 citations

    2

    h-index

  5. 1

    h-index

1–5