Point Cloud Compression

Point Cloud Compression is a computational technique that reduces the size of point cloud data by eliminating spatial and structural redundancies while preserving geometric fidelity, enabling efficient storage and transmission in LiDAR-based perception systems.

347

Publications

18.3K

Citations

759

Authors

284

Institutions

Publications per year

2017–2026

293

Authors

759

  1. WG
    Wei Gao

    Peking University, China

    37 publications · 1.3K citations

    26

    h-index

  2. ZL
    Zhu Li

    University of Missouri–Kansas City, United States

    27 publications · 2.2K citations

    21

    h-index

  3. SL
    Shan Liu

    Peking University, China

    21 publications · 909 citations

    20

    h-index

  4. GL
    Ge Li

    Peking University, China

    21 publications · 893 citations

    19

    h-index

  5. LL
    Li Li

    University of Science and Technology of China, China

    18 publications · 833 citations

    17

    h-index

1–5 of 759

Venues

  1. IEEE Transactions on Image Processing

    24 publications · 2K citations

    21

    h-index

  2. 17

    h-index

  3. IEEE Transactions on Multimedia

    16 publications · 924 citations

    15

    h-index

  4. IEEE Access

    11 publications · 256 citations

    10

    h-index

  5. arXiv (Cornell University)

    Repository · 9 publications · 218 citations

    9

    h-index

1–5