Point Cloud Processing
Point Cloud Processing is a computational methodology within robot perception that involves the analysis, filtering, segmentation, and interpretation of unstructured three-dimensional point sets acquired from environmental sensing to enable spatial understanding and navigation.
13.3K
Publications
841.2K
Citations
28.7K
Authors
3.6K
Institutions
Publications per year
2017–2026
8.3K
13.3K
Speeded-Up Robust Features (SURF)
Herbert Bay, Andreas Ess, Tinne Tuytelaars et al. · Computer Vision and Image Understanding · 2008 · 13.4K citations
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Raffaelli Charles, Hao Su, Kaichun Mo et al. · 2017 · 9.8K citations
Dynamic Graph CNN for Learning on Point Clouds
Yue Wang, Yongbin Sun, Ziwei Liu et al. · ACM Transactions on Graphics · 2019 · 6.7K citations · Full text
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu, Steve Cousins · 2011 · 4.8K citations
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28.7K
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3.6K
Wuhan University
Wuhan, China
68 h-index
1.2K publications
55K citations
Tsinghua University
Beijing, China
67 h-index
635 publications
59.8K citations
ETH Zurich
Zurich, Switzerland
65 h-index
409 publications
78.3K citations
Carnegie Mellon University
Pittsburgh, United States
62 h-index
404 publications
59.2K citations

Chinese Academy of Sciences
Beijing, China
58 h-index
716 publications
36.6K citations
Technical University of Munich
Munich, Germany
57 h-index
608 publications
56.7K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
55 h-index
301 publications
60.9K citations
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing
Wuhan, China
54 h-index
509 publications
25.7K citations
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229 publications · 18.9K citations
78
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Repository · 504 publications · 27.5K citations
71
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469 publications · 24.6K citations
67
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TPAMI · 144 publications · 50.1K citations
61
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212 publications · 12.7K citations
59
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