Point Cloud Processing

Point Cloud Processing is a computational methodology within robot perception that involves the analysis, filtering, segmentation, and interpretation of unstructured three-dimensional point sets acquired from environmental sensing to enable spatial understanding and navigation.

13.3K

Publications

841.2K

Citations

28.7K

Authors

3.6K

Institutions

Publications per year

2017–2026

8.3K

Authors

28.7K

  1. JL
    Jonathan Li

    University of Waterloo, Canada

    123 publications · 7.2K citations

    47

    h-index

  2. CW
    Cheng Wang

    Xiamen University, China

    102 publications · 5.3K citations

    41

    h-index

  3. LG
    Leonidas Guibas

    Stanford University, United States

    51 publications · 22.6K citations

    36

    h-index

  4. BY
    Bisheng Yang

    Wuhan University, China

    57 publications · 3.8K citations

    36

    h-index

  5. US
    Uwe Stilla

    Technical University of Munich, Germany

    75 publications · 3.8K citations

    36

    h-index

1–5 of 28.7K

Venues

  1. ISPRS Journal of Photogrammetry and Remote Sensing

    229 publications · 18.9K citations

    78

    h-index

  2. arXiv (Cornell University)

    Repository · 504 publications · 27.5K citations

    71

    h-index

  3. Remote Sensing

    469 publications · 24.6K citations

    67

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 144 publications · 50.1K citations

    61

    h-index

  5. IEEE Robotics and Automation Letters

    212 publications · 12.7K citations

    59

    h-index

1–5