Michael Niemeyer

PhD Student in Computer Vision / Machine Learning

I’m Michael, a PhD student in the field of machine learning / computer vision in the AVG group at the Max Planck Insitute for Intelligent Systems.

I will finish my PhD at the end of 2022 and I am looking for research scientist positions, preferribly in Germany (Cologne, Stuttgart, Berlin, etc)!

Research Direction: I am excited about complex problems that can be tackled with learning-based systems. Currently, my research focuses on 3D vision, and I am interested in how machines can infer 3D representations from sparse observations. Further, I am big fan of neural scene representations, \ie, how scenes are best represented in learning-based systems using deep neural networks.

Biography: I studied BSc Mathematics at the University of Cologne, Germany. During my Bachelor studies, I spent one year at the University of Barcelona, Spain, funded by the ERASMUS program. Following my interest in computer science, I then moved to Scotland to gain my Master’s degree in Advanced Computer Science at the University of St Andrews. In October 2018, I started my PhD in computer vision / machine learning in the AVG group at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, under the supervision of Andreas Geiger. In 2021 I joined Google Brain for an internship and subsequently as a student researcher.

Awards: In 2011, I graduated as top of my year from secondary school and received the e-fellows scholarship and were admitted to the Germany Mathematics Society and the German Physics Society. In 2017 I received the Dean’s List Award for Academic Excellence for my Master’s degree. Since 2018, I am scholar of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Our two research projects Occupancy Networks and DVR were selected to be among the top-15 most influencial CVPR papers from 2019 and 2020, respectively. In 2021, we received the CS teaching award for our computer vision lecture as well as the AIGameDev scientific award for our GRAF project and the CVPR Best Paper Award for GIRAFFE (news coverage).

Feel free to reach out to me via e-mail for any inquiries!

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Oct 5, 2022 I gave a talk today the 3D Geoinformation Group from the Delft University of Technology (Slides).
Sep 22, 2022 Our two projects MonoSDF and VoxGRAF are accepted to NeurIPS 2022!
Jun 16, 2022 We released the two new preprints MonoSDF and VoxGRAF!
Mar 29, 2022 Our RegNeRF project is accepted as oral to CVPR’22!
Mar 11, 2022 Our RegNeRF project is accepted to CVPR’22! We have now released the code, models, and data.
Jan 13, 2022 I gave a talk at the GAMES Seminar Series with a subsequent panel discussion which was streamed to +400 people.
Dec 7, 2021 I was invited to give a talk at Adobe Research (Slides).
Dec 2, 2021 We released my internship project RegNeRF which I worked on during my time at Google Brain.
Nov 26, 2021 I give a lecture at the TU Munich 3D Vision series about scene representations and neural rendering.
Oct 10, 2021 Our CAMPARI paper got accepted to 3DV’21
Sep 30, 2021 Our Computer Vision Lecture received CS teaching award!
Sep 29, 2021 Our Shapes as Points points paper got accepted to NeurIPS’21 (oral)
Jul 12, 2021 I was invited to give a talk at the AIT lab (ETH) about generative neural scene representations.
Jun 21, 2021 We recieved the CVPR’21 best paper award for our GIRAFFE project
May 12, 2021 I was invited to give a talk about Generative Neural Scene Representations at Amazon Research.

selected publications

Please see the publications tab or Google Scholar for a complete list.

  1. Occupancy Networks: Learning 3D Reconstruction in Function Space
    In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2019
  2. Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
    In Proc. of the IEEE International Conf. on Computer Vision (ICCV) 2019
  3. Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
    In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2020
  4. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
    Schwarz, Katja, Liao, Yiyi, Niemeyer, Michael, and Geiger, Andreas
    In Advances in Neural Information Processing Systems (NeurIPS) 2020
  5. GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
    Niemeyer, Michael, and Geiger, Andreas
    In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2021
  6. RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
    Niemeyer, Michael, Barron, Jonathan T., Mildenhall, Ben, Sajjadi, Mehdi S. M., Geiger, Andreas, and Radwan, Noha
    In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2022