Photo of Varun Burde
Ph.D. Candidate · CTU PragueVisiting Ph.D. Student · Cambridge

Varun Burde

I'm a Ph.D. candidate at the Czech Technical University in Prague, advised by Torsten Sattler and Pavel Burget, and a researcher at the Testbed for Industry 4.0 at CIIRC. I'm currently a visiting Ph.D. student at the University of Cambridge, where I'm grateful to be hosted by Dr. Olaf Wysocki in the Computer Vision for Digital Twins group. Before that, I was fortunate to spend six months at ETH Zürich working with Dr. Xi Wang and Prof. Marc Pollefeys in the Computer Vision and Geometry Group.

Before my Ph.D., I did my M.Sc. in Cybernetics and Robotics at CTU, with a master's thesis on deep neural networks for city mapping using Google Street View data. I received the RICAIP Young Investigator Award in 2022. I'm always happy to hear about research collaborations.

Research

I'm interested in 3D computer vision and robotic manipulation: object-centric 3D reconstruction, 6D object pose estimation, and neural scene representations, including for transparent and textureless objects. Representative papers are highlighted.

  1. Benchmarking the Effects of Object Pose Estimation and Reconstruction on Robotic Grasping Success

    V. Burde, P. Burget, T. Sattler

    ICRA 2026

    A benchmark that ties the quality of 3D reconstruction and 6D pose estimation to what matters downstream: whether a real robot's grasp succeeds.

  2. Figure from OPFormer: Object Pose Estimation Leveraging Foundation Model with Geometric Encoding
    OPFormer: Object Pose Estimation Leveraging Foundation Model with Geometric Encoding

    A. Moroz, V. Zeman, M. Mikšík, E. Isianova, M. David, P. Burget, V. Burde

    WACV 2026

    Object pose estimation that combines features from a vision foundation model with a geometric encoding of the object.

  3. Figure from Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation
    Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

    V. Burde, A. Benbihi, P. Burget, T. Sattler

    WACV 2025

    Which 3D reconstruction method should you use to build object models for pose estimation? A systematic comparison on the downstream task.

  4. Object Pose Estimation Using Implicit Representation for Transparent Objects

    V. Burde, A. Moroz, V. Zeman, P. Burget

    ECCV-W 2024 (Spotlight)

    Implicit object representations for 6D pose estimation of transparent objects, which break most methods. Datasets released.

  5. A CAD Model-Based Navigation Method for UAVs around Transmission Tower

    V. Burde, J. Soucek, A. Lazarov, P. Burget

    CASE 2024

    Using the tower's CAD model to localize and navigate a UAV around power transmission towers.

  6. Figure from Automatic Workspace Calibration Using Homography for Pick and Place
    Automatic Workspace Calibration Using Homography for Pick and Place

    V. Burde, D. S. Martinez Lema, V. Zeman, L. Kahlert, T. Jochman, P. Burget

    CASE 2023

    Automatic workspace calibration with a homography, replacing tedious camera-robot calibration in pick-and-place setups.

News

Experience

  • Sep 2026 – Mar 2027

    Visiting Ph.D. Student · University of Cambridge, United Kingdom

    Hosted by Dr. Olaf Wysocki in the Computer Vision for Digital Twins (CV4DT) group. Minimal viable 3D representations for robotic object manipulation.

  • Mar 2026 – Sep 2026

    Visiting Ph.D. Student · ETH Zürich, Switzerland

    Hosted by Prof. Marc Pollefeys and Dr. Xi Wang in the Computer Vision and Geometry Group (CVG). 3D lifting of objects from egocentric video.

  • Sep 2021 – Present

    Doctoral Researcher · CIIRC, Testbed for Industry 4.0, Prague

    Developed 3D reconstruction and 6D pose estimation pipelines for industrial pick-and-place under real-world conditions, including transparent and textureless objects. Designed benchmarks linking perception quality to downstream robotic grasping success.

  • Oct 2019 – Aug 2021

    Graduate Student Researcher · CIIRC, CMP, Prague

    Built SLAM and path-planning systems for autonomous indoor robots, including a GUI-based navigation interface deployed on real hardware.

Education

Service & Awards

  • Reviewer for NeurIPS, ECCV, ICRA, IROS, IEEE CASE, IEEE Transactions on Robotics (T-RO)
  • Session Chair, Optimized UAV Systems — IEEE CASE, 2024
  • RICAIP Young Investigator Award, 2022

Mentoring

Master's theses supervised at CTU Prague.