Runyu Ding
Impact in
- Computational Mechanics top 2%
- 3D Shape Modeling and Analysis
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
Papers in
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- 3D Shape Modeling and Analysis 4
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- Advanced Neural Network Applications 4
- Multimodal Machine Learning Applications 3
- Co-authors
- Xiaojuan Qi (5 shared papers)Hengshuang Zhao (1 shared paper)Mutian Xu (1 shared paper)Jihan Yang (4 shared papers)Wenqing Zhang (2 shared papers)Song Bai (2 shared papers)Chuhui Xue (2 shared papers)Zhe Wang (1 shared paper)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Science Robotics (1 paper)Lecture notes in computer science (1 paper)neural information processing systems (1 paper)IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (3 papers)
In The Last Decade
Runyu Ding
6 papers receiving 556 citations
Runyu Ding's Hit Papers
Peers
Comparison fields: 5 of 34
- Computational Mechanics 405
- Computer Vision and Pattern Recognition 201
- Geology 18
- Artificial Intelligence 71
- Environmental Engineering 11
Countries citing papers authored by Runyu Ding
This map shows the geographic impact of Runyu Ding'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 Runyu Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Runyu Ding more than expected).
Fields of papers citing papers by Runyu Ding
This network shows the impact of papers produced by Runyu Ding. 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 Runyu Ding. The network helps show where Runyu Ding may publish in the future.
Co-authors
The 9 scholars most cited alongside Runyu Ding, 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 | PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds Hit paper breakdown → | 2021 | 383 |
| 2 | 2023 | 90 | |
| 3 | 2024 | 37 | |
| 4 | 2022 | 23 | |
| 5 | 2022 | 15 | |
| 6 | 2024 | 14 | |
| 7 | 2026 | 0 |
About Runyu Ding
Runyu Ding is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering and Nuclear Energy and Engineering, having authored 7 papers that have together received 562 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (4 papers), Advanced Neural Network Applications (4 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (1 paper), Human Motion and Animation (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Computational Mechanics (405 citations), Computer Vision and Pattern Recognition (201 citations), Geology (18 citations), Artificial Intelligence (71 citations) and Environmental Engineering (11 citations). Runyu Ding has collaborated with scholars based in Hong Kong, China and Germany. Frequent co-authors include Xiaojuan Qi, Hengshuang Zhao, Mutian Xu, Jihan Yang, Wenqing Zhang, Song Bai, Chuhui Xue, Zhe Wang and Li Jiang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Science Robotics, Lecture notes in computer science, neural information processing systems and IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings.
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.