Point Cloud Denoising

Point Cloud Denoising is a point cloud processing technique that removes spurious or non-representative points and measurement artifacts from raw LiDAR-derived point cloud data while preserving the underlying geometric structure and semantic integrity of the scanned objects or environments.

124

Publications

5.8K

Citations

393

Authors

167

Institutions

Publications per year

2017–2026

101

Authors

393

  1. GC
    Gene Cheung

    National Institute of Informatics, Japan

    8 publications · 488 citations

    8

    h-index

  2. MW
    Mingqiang Wei

    Nanjing University of Aeronautics and Astronautics, China

    7 publications · 263 citations

    7

    h-index

  3. IV
    Ivan V. Bajić

    Simon Fraser University, Canada

    5 publications · 184 citations

    5

    h-index

  4. CD
    Chinthaka Dinesh

    Simon Fraser University, Canada

    5 publications · 184 citations

    5

    h-index

  5. JW
    Jun Wang

    Nanjing University of Aeronautics and Astronautics, China

    5 publications · 225 citations

    5

    h-index

1–5 of 393

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 86 citations

    5

    h-index

  2. Remote Sensing

    5 publications · 144 citations

    5

    h-index

  3. Computer Graphics Forum

    5 publications · 558 citations

    5

    h-index

  4. IEEE Access

    5 publications · 241 citations

    5

    h-index

  5. Sensors

    5 publications · 153 citations

    5

    h-index

1–5