2026 Rene Ranftl: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Rising Stars	32	961	957	156	154	53	9287

Research.com Recognitions

2025 - Research.com Rising Stars Award

Overview

Rene Ranftl is affiliated with Intel in the United States. Their research spans several main fields, including Computer Science and Engineering. The primary subfields focus on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Aerospace Engineering, and Industrial and Manufacturing Engineering.

Their work covers a range of specific topics such as Advanced Neural Network Applications, Advanced Vision and Imaging, Domain Adaptation and Few-Shot Learning, Optical Measurement and Interference Techniques, Image Processing Techniques and Applications, Robotics and Sensor-Based Localization, and Machine Learning and Extreme Learning Machines (ELM).

Some of Rene Ranftl's recent publications include:

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer (2022), Repository for Publications and Research Data (ETH Zurich)
Language-driven Semantic Segmentation (2022), arXiv (Cornell University)
Vision Transformers for Dense Prediction (2021), 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Learning high-speed flight in the wild (2021), Zurich Open Repository and Archive (University of Zurich)
How to Train Your Super-Net: An Analysis of Training Heuristics in Weight-Sharing NAS (2020), arXiv (Cornell University)

Frequent coauthors collaborating on multiple publications with Ranftl include Vladlen Koltun, Kaicheng Yu, Mathieu Salzmann, Antonio Loquercio, and Elia Kaufmann.

The majority of Rene Ranftl's work has appeared in the venue arXiv (Cornell University), followed by publications in the Repository for Publications and Research Data (ETH Zurich), the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), and the Zurich Open Repository and Archive (University of Zurich). There is also a publication listed in The International Journal of Robotics Research.

Best Publications

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

Katrin Lasinger;René Ranftl;Konrad Schindler;Vladlen Koltun

1189
Citations
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer.

Rene Ranftl;Katrin Lasinger;David Hafner;Konrad Schindler

1115
Citations
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation

David Ferstl;Christian Reinbacher;Rene Ranftl;Matthias Ruether

618
Citations
High Speed and High Dynamic Range Video with an Event Camera

Henri Rebecq;Rene Ranftl;Vladlen Koltun;Davide Scaramuzza

421
Citations
What Do Single-View 3D Reconstruction Networks Learn?

Maxim Tatarchenko;Stephan R. Richter;Rene Ranftl;Zhuwen Li

386
Citations
Events-To-Video: Bringing Modern Computer Vision to Event Cameras

Henri Rebecq;Rene Ranftl;Vladlen Koltun;Davide Scaramuzza

295
Citations
Learning high-speed flight in the wild.

Antonio Loquercio;Elia Kaufmann;René Ranftl;Matthias Müller

254
Citations
Accurate Optical Flow via Direct Cost Volume Processing

Jia Xu;Rene Ranftl;Vladlen Koltun

217
Citations
Dense Monocular Depth Estimation in Complex Dynamic Scenes

Rene Ranftl;Vibhav Vineet;Qifeng Chen;Vladlen Koltun

206
Citations
Deep Fundamental Matrix Estimation

René Ranftl;Vladlen Koltun

179
Citations
Where Should I Walk? Predicting Terrain Properties From Images Via Self-Supervised Learning

Lorenz Wellhausen;Alexey Dosovitskiy;Rene Ranftl;Krzysztof Walas

175
Citations
Non-local Total Generalized Variation for Optical Flow Estimation

René Ranftl;Kristian Bredies;Thomas Pock;Thomas Pock

162
Citations
Pushing the limits of stereo using variational stereo estimation

Rene Ranftl;Stefan Gehrig;Thomas Pock;Horst Bischof

153
Citations
Insights Into Analysis Operator Learning: From Patch-Based Sparse Models to Higher Order MRFs

Yunjin Chen;Rene Ranftl;Thomas Pock

134
Citations
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

Elia Kaufmann;Mathias Gehrig;Philipp Foehn;Rene Ranftl

125
Citations
Feedback MPC for Torque-Controlled Legged Robots

Ruben Grandia;Farbod Farshidian;Rene Ranftl;Marco Hutter

125
Citations
Deep Drone Racing: From Simulation to Reality With Domain Randomization

Antonio Loquercio;Elia Kaufmann;Rene Ranftl;Alexey Dosovitskiy

123
Citations
Deep Drone Racing: Learning Agile Flight in Dynamic Environments

Elia Kaufmann;Antonio Loquercio;René Ranftl;Alexey Dosovitskiy

108
Citations
Deep Drone Acrobatics

Elia Kaufmann;Antonio Loquercio;René Ranftl;Matthias Müller

106
Citations
Bilevel optimization with nonsmooth lower level problems

Peter Ochs;René Ranftl;Thomas Brox;Thomas Pock;Thomas Pock

77
Citations

Frequent Co-Authors

Vladlen Koltun Apple (United States)

Thomas Pock Graz University of Technology

Davide Scaramuzza University of Zurich

Marco Hutter ETH Zurich

Alexey Dosovitskiy Google (United States)

Horst Bischof Graz University of Technology

Matthias A. Müller University of Stuttgart

Mathieu Salzmann École Polytechnique Fédérale de Lausanne

Thomas Brox University of Freiburg

Konrad Schindler ETH Zurich

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