Joseph G. Lambourne
- Computational Mechanics top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Graphics and Computer-Aided Design top 2%
- Industrial and Manufacturing Engineering top 5%
- Geology top 10%
- Co-authors
- Hang ChuAditya SanghiKarl D. D. WillisChin-Yi ChengYe WangYewen PuPradeep Kumar JayaramanWojciech Matusik
- Topics
- 3D Shape Modeling and Analysis (10 papers)Computer Graphics and Visualization Techniques (5 papers)Manufacturing Process and Optimization (4 papers)
- Journals
- ACM Transactions on GraphicsJournal of Computing and Information Science in Engineering2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- United StatesCanadaSouth Korea
In The Last Decade
Joseph G. Lambourne
11 papers receiving 336 citations
Hit Papers
Peers
Comparison fields: 5 of 47
- Computational Mechanics 209
- Computer Vision and Pattern Recognition 177
- Computer Graphics and Computer-Aided Design 103
- Industrial and Manufacturing Engineering 100
- Geology 61
Countries citing papers authored by Joseph G. Lambourne
This map shows the geographic impact of Joseph G. Lambourne'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 Joseph G. Lambourne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph G. Lambourne more than expected).
Fields of papers citing papers by Joseph G. Lambourne
This network shows the impact of papers produced by Joseph G. Lambourne. 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 Joseph G. Lambourne. The network helps show where Joseph G. Lambourne may publish in the future.
Co-authorship network of co-authors of Joseph G. Lambourne
This figure shows the co-authorship network connecting the top 25 collaborators of Joseph G. Lambourne. A scholar is included among the top collaborators of Joseph G. Lambourne based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joseph G. Lambourne. Joseph G. Lambourne is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 8 | |
| 3 | CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationbreakdown → | 130 |
| 4 | 36 | |
| 5 | 30 | |
| 6 | 81 | |
| 7 | 2 | |
| 8 | 35 | |
| 9 | 2 | |
| 10 | UV-Net: Learning from Curve-Networks and Solids. | 5 |
| 11 | 3 |
About Joseph G. Lambourne
Joseph G. Lambourne is a scholar working on Computer Graphics and Computer-Aided Design, Computational Mechanics and Industrial and Manufacturing Engineering, having authored 11 papers that have together received 349 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (10 papers), Computer Graphics and Visualization Techniques (5 papers) and Manufacturing Process and Optimization (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (103 citations), Geology (61 citations) and Computational Mechanics (209 citations). Joseph G. Lambourne has collaborated with scholars based in United States, Canada and South Korea. Frequent co-authors include Hang Chu, Aditya Sanghi, Karl D. D. Willis, Chin-Yi Cheng, Ye Wang, Yewen Pu, Pradeep Kumar Jayaraman, Wojciech Matusik, Armando Solar-Lezama and Jieliang Luo. Their work appears in journals such as ACM Transactions on Graphics, Journal of Computing and Information Science in Engineering 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.