4D and Quantum Vision Group Max Planck Institute for Informatics


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4D and Quantum Vision

Research Group


About us

Welcome to the web page of the 4D and Quantum Vision (4DQV) group led by Dr. Vladislav Golyanik.

The focus of our team lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of the human body and matching problems on point sets and graphs. We are interested in neural approaches (both supervised and unsupervised), physics-based methods as well as new hardware and sensors (e.g., quantum computers and event cameras). We are integrated in the Visual Computing and Artificial Intelligence Department (D6) and working closely with Prof. Christian Theobalt.

Many research questions at the intersection of computer graphics, computer vision and machine learning involve challenging search problems (e.g., graph matching) or the optimisation of non-convex objectives. For such problems, we develop new algorithmic formulations that can be solved on modern adiabatic quantum annealers or universal quantum computers and investigate which advantages these approaches offer compared to existing classical methods.

Our reserach interests include (but are not limited to):

  • 3D Reconstruction and Tracking of Rigid and Non-Rigid Scenes and Objects
  • Neural Rendering
  • Point Set Registration and Matching Problems
  • Quantum Algorithms for Computer Vision and Graphics
  • Event-based Approaches in Vision and Graphics

Recent Projects


    Advances in Neural Rendering.
    A. Tewari*, J. Thies*, B. Mildenhall*, P. Srinivasan*, E. Tretschk, Y. Wang, C. Lassner, V. Sitzmann, R. Martin-Brualla, S. Lombardi, C. Theobalt, M. Niessner, J. T. Barron, G. Wetzstein, M. Zollhöfer and V. Golyanik.
    * equal contribution.
    State of the Art Report at Eurographics 2022.
    [paper]
This state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D-consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling nonrigidly deforming objects and scene editing and composition. While most of these approaches are scene-specific, we also discuss techniques that generalize across object classes and can be used for generative tasks. In addition to reviewing these state-ofthe-art methods, we provide an overview of fundamental concepts and definitions used in the current literature. We conclude with a discussion on open challenges and social implications.








Further projects

People

PostDocs

PhD Students

Visitors

Master thesis students

Alumni

  • Artur Jesslen (Master thesis, 2022)
  • Erik Johnson (Master thesis, 2022)
  • Maximilian Krahn (Bachelor thesis, 2021)

News

Materials

Teaching

Recent Talks

Source Codes

Open Positions at 4DQV

  • Postdoc (all fields in the scope of 4DQV): please get in touch if interested
  • PhD student at 4DQV (Non-Rigid 3D Scene Reconstruction): how to apply
  • PhD student at 4DQV (Neural Rendering): how to apply
  • PhD student at 4DQV (Quantum Computer Vision): how to apply
  • Visiting PhD student/Intern: how to apply

Contact

Vladislav Golyanik [personal page] [e-mail]