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
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101
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48
University of Michigan
Ann Arbor, United States
2 h-index
2 publications
118 citations
Tsinghua University
Beijing, China
2 h-index
7 publications
219 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
6 publications
261 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
2 publications
58 citations
ETH Zurich
Zurich, Switzerland
2 h-index
2 publications
110 citations

Huazhong University of Science and Technology
Wuhan, China
1 h-index
2 publications
168 citations

University of Tehran
Tehran, Iran
1 h-index
1 publications
12 citations

University of Waterloo
Waterloo, Canada
1 h-index
4 publications
60 citations
1–8 of 48
3 publications · 202 citations
3
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Repository · 2 publications · 110 citations
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Repository · 2 publications · 42 citations
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2 publications · 287 citations
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1 publications · 17 citations
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