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
124
Differentiable surface splatting for point-based geometry processing
Yifan Wang, Felice Serena, Shihao Wu et al. · ACM Transactions on Graphics · 2019 · 249 citations · Full text
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Nanjing University of Aeronautics and Astronautics, China
7 publications · 263 citations
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Peking University
Beijing, China
7 h-index
21 publications
1.4K citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
7 h-index
25 publications
1.1K citations

York University
Toronto, Canada
4 h-index
5 publications
594 citations
Nanjing University of Science and Technology
Nanjing, China
4 h-index
16 publications
764 citations
University College London
London, United Kingdom
3 h-index
7 publications
1.1K citations
Tianjin University
Tianjin, China
3 h-index
10 publications
270 citations
Simon Fraser University
Burnaby, Canada
3 h-index
6 publications
320 citations
Ministry of Natural Resources
Beijing, China
3 h-index
10 publications
214 citations
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Repository · 5 publications · 86 citations
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5 publications · 144 citations
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5 publications · 558 citations
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5 publications · 241 citations
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5 publications · 153 citations
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