Beibei Wang

483 citations
58 papers · 253 · h-index 9

Impact in

Papers in

Beibei Wang

48 papers receiving 247 citations

Peers

Beibei Wang
Comparison fields: 5 of 39
  • Computer Graphics and Computer-Aided Design 131
  • Computer Vision and Pattern Recognition 202
  • Computational Mechanics 91
  • Media Technology 31
  • Signal Processing 19
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Brian Budge United States
Matthew DuVall United States
Thu Nguyen-Phuoc United States
Yuval Bahat Israel
Daniel Erickson United States
Siavash Bigdeli Switzerland
Sylvain Paris United States
Weihao Xia China
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Marco V. Bernardo Portugal
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Citations per field
00.5×3.9×
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Citations per year

Countries citing papers authored by Beibei Wang

Since Specialization
Citations

This map shows the geographic impact of Beibei Wang'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 Beibei Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beibei Wang more than expected).

Fields of papers citing papers by Beibei Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Beibei Wang. 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 Beibei Wang. The network helps show where Beibei Wang may publish in the future.

Co-authors

The 25 scholars most cited alongside Beibei Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Beibei Wang Line = papers co-authored together Beibei Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200521
2 202214
3 202013
4 202213
5 200613
6 201412
7 202010
8 20189
9 20228
10 20208
11 20247
12 20247
13 20077
14 20227
15 20236
16 20206
17 20116
18 20206
19 20236
20 20226

About Beibei Wang

Beibei Wang is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Media Technology and Signal Processing, having authored 58 papers that have together received 253 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (32 papers), Advanced Vision and Imaging (31 papers), 3D Shape Modeling and Analysis (13 papers), Advanced Image Processing Techniques (12 papers), Image Enhancement Techniques (9 papers), Image Processing Techniques and Applications (5 papers), Image and Signal Denoising Methods (5 papers) and Advanced Data Compression Techniques (5 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (131 citations), Computer Vision and Pattern Recognition (202 citations), Computational Mechanics (91 citations), Media Technology (31 citations) and Signal Processing (19 citations). Beibei Wang has collaborated with scholars based in China, United States and France. Frequent co-authors include Nicolas Holzschuch, Ling‐Qi Yan, Miloš Hašan, Yao Wang, Ivan Selesnick, Anthony Vetro, Lu Wang, Ran Ding, Ligang Liu and Dekun Zou. Their work appears in journals such as ACM Transactions on Graphics, Computer Graphics Forum, IEEE Transactions on Visualization and Computer Graphics, Computational Visual Media and Review of Scientific Instruments.

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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