Kaichun Mo
Impact in
- Geology top 0.05%
- 3D Surveying and Cultural Heritage
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- Computer Graphics and Visualization Techniques
Papers in
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- 3D Shape Modeling and Analysis 12
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- Human Pose and Action Recognition 4
- Advanced Vision and Imaging 3
- Multimodal Machine Learning Applications 2
- Co-authors
- Leonidas Guibas (11 shared papers)Hao Su (2 shared papers)Li Yi (2 shared papers)Paul Guerrero (1 shared paper)Niloy J. Mitra (1 shared paper)Peter Wonka (1 shared paper)Shubham Tulsiani (1 shared paper)Abhinav Gupta (1 shared paper)
- Journals
- ACM Transactions on Graphics (3 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)arXiv (Cornell University) (1 paper)Open MIND (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Kaichun Mo
15 papers receiving 6.0k citations
Kaichun Mo's Hit Papers
Peers
Comparison fields: 5 of 147
- Geology 2.1k
- Computer Graphics and Computer-Aided Design 959
- Computer Vision and Pattern Recognition 3.2k
- Computational Mechanics 2.8k
- Environmental Engineering 1.6k
Countries citing papers authored by Kaichun Mo
This map shows the geographic impact of Kaichun Mo'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 Kaichun Mo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaichun Mo more than expected).
Fields of papers citing papers by Kaichun Mo
This network shows the impact of papers produced by Kaichun Mo. 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 Kaichun Mo. The network helps show where Kaichun Mo may publish in the future.
Co-authors
The 25 scholars most cited alongside Kaichun Mo, 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 | PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Hit paper breakdown → | 2017 | 5935 |
| 2 | 2019 | 141 | |
| 3 | 2021 | 79 | |
| 4 | 2023 | 25 | |
| 5 | 2022 | 24 | |
| 6 | DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry | 2020 | 15 |
| 7 | 2024 | 6 | |
| 8 | 2024 | 6 | |
| 9 | Generative 3D Part Assembly via Dynamic Graph Learning | 2020 | 4 |
| 10 | 2023 | 4 | |
| 11 | 2022 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2023 | 3 | |
| 14 | 2023 | 3 | |
| 15 | 2023 | 2 | |
| 16 | 2025 | 0 |
About Kaichun Mo
Kaichun Mo is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Control and Systems Engineering and Geology, having authored 16 papers that have together received 6.3k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (12 papers), Computer Graphics and Visualization Techniques (7 papers), Human Pose and Action Recognition (4 papers), 3D Surveying and Cultural Heritage (4 papers), Advanced Vision and Imaging (3 papers), Multimodal Machine Learning Applications (2 papers), Robotics and Sensor-Based Localization (2 papers) and Human Motion and Animation (2 papers). The work is most often cited by research in Geology (2.1k citations), Computer Graphics and Computer-Aided Design (959 citations), Computer Vision and Pattern Recognition (3.2k citations), Computational Mechanics (2.8k citations) and Environmental Engineering (1.6k citations). Kaichun Mo has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Leonidas Guibas, Hao Su, Li Yi, Paul Guerrero, Niloy J. Mitra, Peter Wonka, Shubham Tulsiani, Abhinav Gupta, Mustafa Mukadam and Yu‐Kun Lai. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Open MIND.
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.