Qingyong Hu

7.2k citations
43 papers · 4.0k indexed · 5 hit papers · h-index 16
Topics
3D Shape Modeling and Analysis (17 papers)3D Surveying and Cultural Heritage (15 papers)Video Surveillance and Tracking Methods (10 papers)

In The Last Decade

Qingyong Hu

41 papers receiving 3.9k citations

Hit Papers

Deep Learning for 3D Point Clouds: A Survey2020202620222024202020202022202120214008001.2k

Peers

Qingyong Hu
Comparison fields: 5 of 124
  • Geology 1.9k
  • Computational Mechanics 1.9k
  • Environmental Engineering 1.7k
  • Computer Vision and Pattern Recognition 1.7k
  • Aerospace Engineering 931
Replace Yongbin Sun with:
Yongbin Sun China
Kaichun Mo United States
Daniel Maturana United States
Hanyun Wang China
Nico Blodow Germany
Angela Dai Germany
Linguang Zhang United States
Stefano Rosa Italy
Jaesik Park South Korea
Andrei Sharf Israel
Qingyong Hu relative to Yongbin Sun China Yongbin Sun's profile →
Citations per field
00.5×1.5×
Yongbin Sun · 1×
Citations per year

Countries citing papers authored by Qingyong Hu

Since Specialization
Citations

This map shows the geographic impact of Qingyong Hu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Qingyong Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingyong Hu more than expected).

Fields of papers citing papers by Qingyong Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qingyong Hu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Qingyong Hu. The network helps show where Qingyong Hu may publish in the future.

Co-authorship network of co-authors of Qingyong Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Qingyong Hu. A scholar is included among the top collaborators of Qingyong Hu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Qingyong Hu. Qingyong Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 5
3 7
4 3
5 8
6 64
7 11
8 2
9 15
10 9
11 15
12 96
13 21
14
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudsbreakdown →
1288
15
Deep Learning for 3D Point Clouds: A Surveybreakdown →
1394
16 21
17
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
36
18 10
19 22
20 9

About Qingyong Hu

Qingyong Hu is a scholar working on Geology, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 43 papers that have together received 4.0k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (17 papers), 3D Surveying and Cultural Heritage (15 papers) and Video Surveillance and Tracking Methods (10 papers). The work is most often cited by research in Geology (1.9k citations), Computer Graphics and Computer-Aided Design (430 citations) and Environmental Engineering (1.7k citations). Qingyong Hu has collaborated with scholars based in China, United Kingdom and Hong Kong. Frequent co-authors include Yulan Guo, Andrew Markham, Bo Yang, Hao Liu, Hanyun Wang, Mohammed Bennamoun, Li Liu, Niki Trigoni, Stefano Rosa and Linhai Xie. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and IEEE Transactions on Image Processing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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