Junshu Tang

404 citations
12 papers · 214 · 1 hit paper · h-index 6

Impact in

Papers in

Junshu Tang

11 papers receiving 213 citations

Junshu Tang's Hit Papers

Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior 2023 · 104 citations
1040+1+2Years since publication255075100

Peers

Junshu Tang
Comparison fields: 5 of 36
  • Computer Graphics and Computer-Aided Design 91
  • Geology 45
  • Computer Vision and Pattern Recognition 134
  • Computational Mechanics 109
  • Environmental Engineering 18
Replace Anpei Chen with:
Anpei Chen Germany
Oscar Michel United States
Matt Deitke United States
Taoran Yi China
Christian Reiser Germany
Frank Perbet Japan
Fenggen Yu China
Boyang Deng United States
Yuanbo Xiangli China
Junshu Tang relative to Anpei Chen Germany Anpei Chen's profile →
Citations per field
00.5×1.6×
Anpei Chen · 1×
Citations per year

Countries citing papers authored by Junshu Tang

Since Specialization
Citations

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

Fields of papers citing papers by Junshu Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Junshu Tang, 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 Junshu Tang Line = papers co-authored together Junshu Tang links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior
Hit paper breakdown →
2023104
2 202252
3 202314
4 202112
5 202110
6 20219
7 20235
8 20214
9 20252
10 20251
11 20241
12 20250

About Junshu Tang

Junshu Tang is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Computer Graphics and Computer-Aided Design, Control and Systems Engineering and Computer Networks and Communications, having authored 12 papers that have together received 214 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (5 papers), 3D Shape Modeling and Analysis (5 papers), Advanced Vision and Imaging (4 papers), Computer Graphics and Visualization Techniques (4 papers), Face recognition and analysis (3 papers), Human Motion and Animation (2 papers), Human Pose and Action Recognition (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (91 citations), Geology (45 citations), Computer Vision and Pattern Recognition (134 citations), Computational Mechanics (109 citations) and Environmental Engineering (18 citations). Junshu Tang has collaborated with scholars based in China, Hong Kong and United Kingdom. Frequent co-authors include Lizhuang Ma, Ran Yi, Bo Zhang, Tengfei Wang, Ting Zhang, Dong Chen, Yuan Xie, Zhijun Gong, Zhiwen Shao and Xin Tan. Their work appears in journals such as The Visual Computer, Applied Intelligence, IEEE Transactions on Visualization and Computer Graphics, IEEE Multimedia and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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