Andreas Lehrmann

1.1k citations
8 papers · 562 · 1 hit paper · h-index 4

Impact in

Papers in

Andreas Lehrmann

8 papers receiving 544 citations

Hit Papers

Neural volumes 2019 · 418 citations
4180+2+4Years since publication100200300400

Peers

Andreas Lehrmann
Comparison fields: 5 of 52
  • Computer Graphics and Computer-Aided Design 277
  • Computer Vision and Pattern Recognition 492
  • Computational Mechanics 231
  • Human-Computer Interaction 24
  • Geology 22
Replace Michael Zollhoefer with:
Michael Zollhoefer United States
Yuanqing Zhang China
Qing Shuai China
Fanbo Xiang United States
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Jean-Sébastien Franco France
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Kripasindhu Sarkar Germany
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Citations per field
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Citations per year

Countries citing papers authored by Andreas Lehrmann

Since Specialization
Citations

This map shows the geographic impact of Andreas Lehrmann'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 Andreas Lehrmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Lehrmann more than expected).

Fields of papers citing papers by Andreas Lehrmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andreas Lehrmann. 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 Andreas Lehrmann. The network helps show where Andreas Lehrmann may publish in the future.

Co-authors

The 14 scholars most cited alongside Andreas Lehrmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Andreas Lehrmann Line = papers co-authored together Andreas Lehrmann links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
Neural volumes
Hit paper breakdown →
2019418
2 2014107
3 201327
4 20124
5
PROVIDE: A Probabilistic Framework for Unsupervised Video Decomposition
20212
6
Non-parametric Structured Output Networks
20172
7
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
20181
8
Structural Decompositions for End-to-End Relighting.
20191

About Andreas Lehrmann

Andreas Lehrmann is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Graphics and Computer-Aided Design, Molecular Biology and Control and Systems Engineering, having authored 8 papers that have together received 562 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (3 papers), Human Pose and Action Recognition (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Computer Graphics and Visualization Techniques (2 papers), Machine Learning and Data Classification (2 papers), Cell Image Analysis Techniques (1 paper), Video Surveillance and Tracking Methods (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (277 citations), Computer Vision and Pattern Recognition (492 citations), Computational Mechanics (231 citations), Human-Computer Interaction (24 citations) and Geology (22 citations). Andreas Lehrmann has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Jason Saragih, Yaser Sheikh, Stephen Lombardi, Tomas Simon, Gabriel Schwartz, Peter Gehler, Sebastian Nowozin, Leonid Sigal, Polina Zablotskaia and Michael Huber. Their work appears in journals such as ACM Transactions on Graphics, Data Mining and Knowledge Discovery, International Conference on Learning Representations, Uncertainty in Artificial Intelligence and arXiv (Cornell University).

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.

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