Kangle Deng
Impact in
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- Computer Graphics and Visualization Techniques
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- Advanced Vision and Imaging
- Advanced Image Processing Techniques
- Generative Adversarial Networks and Image Synthesis
- Optical measurement and interference techniques
Papers in
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- Advanced Vision and Imaging 4
- Generative Adversarial Networks and Image Synthesis 3
- Multimodal Machine Learning Applications 1
- Video Analysis and Summarization 1
- Optical measurement and interference techniques 1
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- Computer Graphics and Visualization Techniques 4
- Co-authors
- Deva Ramanan (5 shared papers)Jun-Yan Zhu (5 shared papers)Andrew Liu (1 shared paper)Yuxin Peng (1 shared paper)Tianyi Fei (1 shared paper)Xin Huang (1 shared paper)Gengshan Yang (3 shared papers)Alexander Weiß (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (1 paper)Lecture notes in computer science (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesIsraelChina
In The Last Decade
Kangle Deng
7 papers receiving 599 citations
Kangle Deng's Hit Papers
Peers
Comparison fields: 5 of 47
- Computer Graphics and Computer-Aided Design 291
- Computer Vision and Pattern Recognition 502
- Geology 52
- Computational Mechanics 187
- Instrumentation 16
Countries citing papers authored by Kangle Deng
This map shows the geographic impact of Kangle Deng'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 Kangle Deng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kangle Deng more than expected).
Fields of papers citing papers by Kangle Deng
This network shows the impact of papers produced by Kangle Deng. 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 Kangle Deng. The network helps show where Kangle Deng may publish in the future.
Co-authors
The 18 scholars most cited alongside Kangle Deng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Depth-supervised NeRF: Fewer Views and Faster Training for Free Hit paper breakdown → | 2022 | 545 |
| 2 | 2019 | 32 | |
| 3 | 2023 | 16 | |
| 4 | 2024 | 10 | |
| 5 | 2023 | 8 | |
| 6 | 2024 | 3 | |
| 7 | 2020 | 1 | |
| 8 | 2024 | 0 | |
| 9 | 2025 | 0 |
About Kangle Deng
Kangle Deng is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Signal Processing and Mechanical Engineering, having authored 9 papers that have together received 615 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (4 papers), Computer Graphics and Visualization Techniques (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers), 3D Shape Modeling and Analysis (3 papers), Multimodal Machine Learning Applications (1 paper), Video Analysis and Summarization (1 paper), Optical measurement and interference techniques (1 paper) and Music and Audio Processing (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (291 citations), Computer Vision and Pattern Recognition (502 citations), Geology (52 citations), Computational Mechanics (187 citations) and Instrumentation (16 citations). Kangle Deng has collaborated with scholars based in United States, Israel and China. Frequent co-authors include Deva Ramanan, Jun-Yan Zhu, Andrew Liu, Yuxin Peng, Tianyi Fei, Xin Huang, Gengshan Yang, Alexander Weiß, Tinghui Zhou and Maneesh Agrawala. Their work appears in journals such as IEEE Robotics and Automation Letters, Lecture notes in computer science, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.