Maxim Tatarchenko
- Computational Mechanics top 1%
- Computer Vision and Pattern Recognition top 1%
- Geology top 1%
- Computer Graphics and Computer-Aided Design top 0.5%
- Environmental Engineering top 5%
- Co-authors
- Thomas BroxAlexey DosovitskiyVladlen KoltunQian-Yi ZhouJaesik ParkStephan R. RichterRené RanftlZhuwen Li
- Topics
- 3D Shape Modeling and Analysis (5 papers)Advanced Vision and Imaging (3 papers)3D Surveying and Cultural Heritage (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer VisionComputer Vision and Image Understanding
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Maxim Tatarchenko
10 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 75
- Computational Mechanics 800
- Computer Vision and Pattern Recognition 673
- Geology 446
- Computer Graphics and Computer-Aided Design 382
- Environmental Engineering 249
Countries citing papers authored by Maxim Tatarchenko
This map shows the geographic impact of Maxim Tatarchenko'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 Maxim Tatarchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxim Tatarchenko more than expected).
Fields of papers citing papers by Maxim Tatarchenko
This network shows the impact of papers produced by Maxim Tatarchenko. 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 Maxim Tatarchenko. The network helps show where Maxim Tatarchenko may publish in the future.
Co-authorship network of co-authors of Maxim Tatarchenko
This figure shows the co-authorship network connecting the top 25 collaborators of Maxim Tatarchenko. A scholar is included among the top collaborators of Maxim Tatarchenko 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 Maxim Tatarchenko. Maxim Tatarchenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 7 | |
| 4 | 1 | |
| 5 | What Do Single-View 3D Reconstruction Networks Learn?breakdown → | 256 |
| 6 | 10 | |
| 7 | 3 | |
| 8 | Tangent Convolutions for Dense Prediction in 3Dbreakdown → | 364 |
| 9 | Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputsbreakdown → | 417 |
| 10 | 144 |
About Maxim Tatarchenko
Maxim Tatarchenko is a scholar working on Geology, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 10 papers that have together received 1.2k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (5 papers), Advanced Vision and Imaging (3 papers) and 3D Surveying and Cultural Heritage (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (382 citations), Geology (446 citations) and Computational Mechanics (800 citations). Maxim Tatarchenko has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Thomas Brox, Alexey Dosovitskiy, Vladlen Koltun, Qian-Yi Zhou, Jaesik Park, Stephan R. Richter, René Ranftl, Zhuwen Li, Jost Tobias Springenberg and Oier Mees. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Computer Vision and Image Understanding.
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.