Matthias Niesner
- Computational Mechanics top 5%
- Computer Vision and Pattern Recognition top 5%
- Geology top 5%
- Computer Graphics and Computer-Aided Design top 2%
- Environmental Engineering top 10%
- Co-authors
- Ji HouBenjamin GrahamSaining XieBo ZhengYinyu NieXiaoguang HanYang LiAngela Dai
- Topics
- Computer Graphics and Visualization Techniques (4 papers)Advanced Vision and Imaging (4 papers)3D Shape Modeling and Analysis (4 papers)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Partner nations
- GermanyUnited StatesUnited Kingdom
In The Last Decade
Matthias Niesner
8 papers receiving 356 citations
Hit Papers
Peers
Comparison fields: 5 of 39
- Computational Mechanics 230
- Computer Vision and Pattern Recognition 205
- Geology 135
- Computer Graphics and Computer-Aided Design 90
- Environmental Engineering 80
Countries citing papers authored by Matthias Niesner
This map shows the geographic impact of Matthias Niesner'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 Matthias Niesner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Niesner more than expected).
Fields of papers citing papers by Matthias Niesner
This network shows the impact of papers produced by Matthias Niesner. 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 Matthias Niesner. The network helps show where Matthias Niesner may publish in the future.
Co-authorship network of co-authors of Matthias Niesner
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Niesner. A scholar is included among the top collaborators of Matthias Niesner 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 Matthias Niesner. Matthias Niesner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contextsbreakdown → | 163 |
| 3 | 58 | |
| 4 | 18 | |
| 5 | 38 | |
| 6 | 20 | |
| 7 | 52 | |
| 8 | 2 |
About Matthias Niesner
Matthias Niesner is a scholar working on Computer Graphics and Computer-Aided Design, Geology and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 361 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (4 papers), Advanced Vision and Imaging (4 papers) and 3D Shape Modeling and Analysis (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (90 citations), Geology (135 citations) and Computational Mechanics (230 citations). Matthias Niesner has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Ji Hou, Benjamin Graham, Saining Xie, Bo Zheng, Yinyu Nie, Xiaoguang Han, Yang Li, Angela Dai, Justus Thies and Yawar Siddiqui. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.