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
347
Danillo B. Graziosi, Ohji Nakagami, S. Kuma et al. · APSIPA Transactions on Signal and Information Processing · 2020 · 461 citations · Full text
Geometric distortion metrics for point cloud compression
Dong Tian, Hideaki Ochimizu, Chen Feng et al. · 2017 · 417 citations
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Peking University
Beijing, China
26 h-index
139 publications
5.3K citations
Peng Cheng Laboratory
Shenzhen, China
23 h-index
60 publications
2.4K citations
University of Missouri–Kansas City
Kansas City, United States
22 h-index
58 publications
3.4K citations
KLA (United States)
Milpitas, United States
15 h-index
18 publications
773 citations
Shandong University
Jinan, China
15 h-index
30 publications
2.5K citations
Universidade de Brasília
Brasília, Brazil
15 h-index
38 publications
2K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
14 h-index
41 publications
2.6K citations
Instituto de Telecomunicações
Lisbon, Portugal
14 h-index
65 publications
2.5K citations
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24 publications · 2K citations
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18 publications · 1.4K citations
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16 publications · 924 citations
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11 publications · 256 citations
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Repository · 9 publications · 218 citations
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