World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
108
Citations
71147
World Ranking
252
National Ranking
139

Research.com Recognitions

  • 2007 - Fellow of Alfred P. Sloan Foundation

Overview

Vladlen Koltun is affiliated with Apple in the United States and has an extensive publication record primarily in computer science and engineering. Their research expertise centers on computer vision, artificial intelligence, and robotics, with significant contributions across various subfields and topics.

The scientist's prominent research areas include:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Aerospace Engineering
  • Biomedical Engineering
  • Statistical and Nonlinear Physics

Key topics addressed in their work encompass:

  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Reinforcement Learning in Robotics
  • Multimodal Machine Learning Applications
  • Advanced Vision and Imaging
  • Robotics and Sensor-Based Localization
  • Advanced Image and Video Retrieval Techniques

Vladlen Koltun has contributed to multiple reputable publication venues, such as:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Repository for Publications and Research Data (ETH Zurich)

Recent publications illustrate the scope and focus of their research:

  • "Point Transformer", 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer", 2022, Repository for Publications and Research Data (ETH Zurich)
  • "Learning quadrupedal locomotion over challenging terrain", 2020, Repository for Publications and Research Data (ETH Zurich)
  • "Learning robust perceptive locomotion for quadrupedal robots in the wild", 2022, Science Robotics
  • "NeRF++: Analyzing and Improving Neural Radiance Fields", 2020, arXiv (Cornell University)

They frequently collaborate with a group of coauthors, including:

  • Ozan Şener
  • Philipp Krähenbühl
  • René Ranftl
  • Xingyi Zhou
  • Erik Wijmans

Among their recognitions, Vladlen Koltun was named a Fellow of the Alfred P. Sloan Foundation in 2007.

Best Publications

  • Multi-Scale Context Aggregation by Dilated Convolutions

    Fisher Yu;Vladlen Koltun

  • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

    Shaojie Bai;J. Zico Kolter;Vladlen Koltun

  • Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

    Philipp Krähenbühl;Vladlen Koltun

  • Direct Sparse Odometry

    Jakob Engel;Vladlen Koltun;Daniel Cremers

  • Playing for Data: Ground Truth from Computer Games

    Stephan R. Richter;Vibhav Vineet;Stefan Roth;Vladlen Koltun

  • Dilated Residual Networks

    Fisher Yu;Vladlen Koltun;Thomas Funkhouser

  • CARLA: An Open Urban Driving Simulator

    Alexey Dosovitskiy;Germán Ros;Felipe Codevilla;Antonio M. López

  • Learning quadrupedal locomotion over challenging terrain

    Joonho Lee;Jemin Hwangbo;Jemin Hwangbo;Lorenz Wellhausen;Vladlen Koltun

  • Tanks and temples: benchmarking large-scale scene reconstruction

    Arno Knapitsch;Jaesik Park;Qian-Yi Zhou;Vladlen Koltun

  • Open3D: A Modern Library for 3D Data Processing

    Qian-Yi Zhou;Jaesik Park;Vladlen Koltun

  • Learning agile and dynamic motor skills for legged robots

    Jemin Hwangbo;Joonho Lee;Alexey Dosovitskiy;Dario Bellicoso

  • Learning to See in the Dark

    Chen Chen;Qifeng Chen;Jia Xu;Vladlen Koltun

  • Tracking Objects as Points

    Xingyi Zhou;Vladlen Koltun;Philipp Krähenbühl

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

    Katrin Lasinger;René Ranftl;Konrad Schindler;Vladlen Koltun

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

    Rene Ranftl;Katrin Lasinger;David Hafner;Konrad Schindler

  • End-to-End Driving Via Conditional Imitation Learning

    Felipe Codevilla;Matthias Miiller;Antonio Lopez;Vladlen Koltun

  • Photographic Image Synthesis with Cascaded Refinement Networks

    Qifeng Chen;Vladlen Koltun

  • Guided Policy Search

    Sergey Levine;Vladlen Koltun

  • Fast Global Registration

    Qian-Yi Zhou;Jaesik Park;Vladlen Koltun

  • Learning robust perceptive locomotion for quadrupedal robots in the wild

    Unknown

  • Habitat: A Platform for Embodied AI Research

    Manolis Savva;Jitendra Malik;Devi Parikh;Dhruv Batra

  • NeRF++: Analyzing and Improving Neural Radiance Fields.

    Kai Zhang;Gernot Riegler;Noah Snavely;Vladlen Koltun

  • Habitat: A Platform for Embodied AI Research

    Manolis Savva;Abhishek Kadian;Oleksandr Maksymets;Yili Zhao

Frequent Co-Authors

Alexey Dosovitskiy
Alexey Dosovitskiy Google (United States)
Rene Ranftl
Rene Ranftl Intel (United States)
Micha Sharir
Micha Sharir Tel Aviv University
Philipp Krähenbühl
Philipp Krähenbühl The University of Texas at Austin
Sergey Levine
Sergey Levine University of California, Berkeley
Davide Scaramuzza
Davide Scaramuzza University of Zurich
Thomas Brox
Thomas Brox University of Freiburg
J. Zico Kolter
J. Zico Kolter Carnegie Mellon University
Boris Aronov
Boris Aronov New York University
Antonio M. López
Antonio M. López Autonomous University of Barcelona

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