Point Cloud Anomaly Detection

Point Cloud Anomaly Detection is a computational method in point cloud processing that identifies statistically rare or semantically inconsistent points or structures within a 3D point cloud by analyzing local or global geometric deviations from expected spatial patterns.

29

Publications

1.3K

Citations

101

Authors

48

Institutions

Publications per year

2017–2026

16

Authors

101

  1. SS
    Soheil Sotoodeh

    ETH Zurich, Switzerland

    2 publications · 110 citations

    2

    h-index

  2. BY
    Bisheng Yang

    Wuhan University, China

    1 publications · 107 citations

    1

    h-index

  3. FM
    Fadel M. Megahed

    Miami University, United States

    1 publications · 57 citations

    1

    h-index

  4. JA
    Javid Akhavan

    Stevens Institute of Technology, United States

    1 publications · 23 citations

    1

    h-index

  5. JV
    Jake Vanderplas

    University of Washington, United States

    1 publications · 77 citations

    1

    h-index

1–5 of 101

Venues

  1. Remote Sensing

    3 publications · 202 citations

    3

    h-index

  2. Repository for Publications and Research Data (ETH Zurich)

    Repository · 2 publications · 110 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 42 citations

    2

    h-index

  4. Pattern Recognition

    2 publications · 287 citations

    2

    h-index

  5. 1

    h-index

1–5