Michael Niemeyer


I am a research scientist at Google working on 3D computer vision and graphics.

Bio: I was a PhD student at the Max Planck Insitute for Intelligent Systems supervised by Andreas Geiger. During my PhD studies, I joined Google Brain for an internship and subsequently as a student researcher. As an undergraduate student, I received a BSc in Mathematics from the University of Cologne (Germany) and a MSc from the University of St Andrews (UK).

Awards: In 2011, I graduated as top of my year from secondary school and received the e-fellows scholarship and was 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. During my PhD studies, I was a 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).

For any inquiries, feel free to reach out to me via mail!

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Publications

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MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger
Advances in Neural Information Processing Systems (NeurIPS), 2022
Project Page / Paper / Supplemental / Code /
@InProceedings{Yu2022NEURIPS, 
	author = {Zehao Yu and Songyou Peng and Michael Niemeyer and Torsten Sattler and Andreas Geiger}, 
	title = {MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction}, 
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, 
	year = {2022}, 
}
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VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids
Katja Schwarz, Axel Sauer, Michael Niemeyer, Yiyi Liao, Andreas Geiger
Advances in Neural Information Processing Systems (NeurIPS), 2022
Project Page / Paper / Supplemental / Code /
@InProceedings{Schwarz2022NEURIPS, 
	author = {Katja Schwarz and Axel Sauer and Michael Niemeyer and Yiyi Liao and Andreas Geiger}, 
	title = {VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids}, 
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, 
	year = {2022}, 
}
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RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs (Oral Presentation)
Michael Niemeyer, Jonathan Barron, Ben Mildenhall, Mehdi Sajjadi, Andreas Geiger, Noha Radwan
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Niemeyer2022CVPR, 
	author = {Michael Niemeyer and Jonathan Barron and Ben Mildenhall and Mehdi Sajjadi and Andreas Geiger and Noha Radwan}, 
	title = {RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs}, 
	booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, 
	year = {2022}, 
}
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Shape As Points: A Differentiable Poisson Solver (Oral Presentation)
Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger
Advances in Neural Information Processing Systems (NeurIPS), 2021
Project Page / Paper / Video / Poster / Code /
@InProceedings{Peng2021NEURIPS, 
	author = {Songyou Peng and Chiyu Jiang and Yiyi Liao and Michael Niemeyer and Marc Pollefeys and Andreas Geiger}, 
	title = {Shape As Points: A Differentiable Poisson Solver}, 
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, 
	year = {2021}, 
}
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GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger
Advances in Neural Information Processing Systems (NeurIPS), 2020
Project Page / Paper / Supplemental / Video / Code /
@InProceedings{Schwarz2020NEURIPS, 
	author = {Katja Schwarz and Yiyi Liao and Michael Niemeyer and Andreas Geiger}, 
	title = {GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis}, 
	booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, 
	year = {2020}, 
}
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GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields (Oral Presentation, Best Paper Award)
Michael Niemeyer, Andreas Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Niemeyer2021CVPR, 
	author = {Michael Niemeyer and Andreas Geiger}, 
	title = {GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields}, 
	booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, 
	year = {2021}, 
}
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CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields
Michael Niemeyer, Andreas Geiger
Proc. of the International Conf. on 3D Vision (3DV), 2021
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Niemeyer2021THREEDV, 
	author = {Michael Niemeyer and Andreas Geiger}, 
	title = {CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields}, 
	booktitle = {Proc. of the International Conf. on 3D Vision (3DV)}, 
	year = {2021}, 
}
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Learning Implicit Surface Light Fields
Michael Oechsle, Michael Niemeyer, Christian Reiser, Lars Mescheder, Thilo Strauss, Andreas Geiger
Proc. of the International Conf. on 3D Vision (3DV), 2020
Project Page / Paper / Supplemental / Poster / Code /
@InProceedings{Oechsle2020THREEDV, 
	author = {Michael Oechsle and Michael Niemeyer and Christian Reiser and Lars Mescheder and Thilo Strauss and Andreas Geiger}, 
	title = {Learning Implicit Surface Light Fields}, 
	booktitle = {Proc. of the International Conf. on 3D Vision (3DV)}, 
	year = {2020}, 
}
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Convolutional Occupancy Networks (Spotlight Presentation)
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger
Proc. of the European Conf. on Computer Vision (ECCV), 2020
Project Page / Paper / Supplemental / Video / Code /
@InProceedings{Peng2020ECCV, 
	author = {Songyou Peng and Michael Niemeyer and Lars Mescheder and Marc Pollefeys and Andreas Geiger}, 
	title = {Convolutional Occupancy Networks}, 
	booktitle = {Proc. of the European Conf. on Computer Vision (ECCV)}, 
	year = {2020}, 
}
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Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2020
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Niemeyer2020CVPR, 
	author = {Michael Niemeyer and Lars Mescheder and Michael Oechsle and Andreas Geiger}, 
	title = {Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision}, 
	booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, 
	year = {2020}, 
}
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Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics
Michael Niemeyer, Lars Mescheder, Michael Oechsle, Andreas Geiger
Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2019
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Niemeyer2019ICCV, 
	author = {Michael Niemeyer and Lars Mescheder and Michael Oechsle and Andreas Geiger}, 
	title = {Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics}, 
	booktitle = {Proc. of the IEEE International Conf. on Computer Vision (ICCV)}, 
	year = {2019}, 
}
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Texture Fields: Learning Texture Representations in Function Space (Oral Presentation)
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, Andreas Geiger
Proc. of the IEEE International Conf. on Computer Vision (ICCV), 2019
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Oechsle2019ICCV, 
	author = {Michael Oechsle and Lars Mescheder and Michael Niemeyer and Thilo Strauss and Andreas Geiger}, 
	title = {Texture Fields: Learning Texture Representations in Function Space}, 
	booktitle = {Proc. of the IEEE International Conf. on Computer Vision (ICCV)}, 
	year = {2019}, 
}
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Occupancy Networks: Learning 3D Reconstruction in Function Space (Oral Presentation, Best Paper Finalist)
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger
Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019
Project Page / Paper / Supplemental / Video / Poster / Code /
@InProceedings{Mescheder2019CVPR, 
	author = {Lars Mescheder and Michael Oechsle and Michael Niemeyer and Sebastian Nowozin and Andreas Geiger}, 
	title = {Occupancy Networks: Learning 3D Reconstruction in Function Space}, 
	booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, 
	year = {2019}, 
}

Talks

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Neural Scene Representations and Differentiable Rendering
Delft University of Technology, 2022
Slides
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Implicit Neural Scene Representations and 3D-Aware Generative Modelling
GAMES Webinar Series, 2022
Slides
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Generative Neural Scene Representations
Adobe Research, 2021
Slides
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Implicit Scene Representations and Neural Rendering
Technical University Munic - AI Lecture Series, 2021
Slides
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Generative Neural Scene Representations for 3D-Aware Image Synthesis
AIT (ETH), 2021
Slides
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Generative Neural Scene Representations for 3D-Aware Image Synthesis
Amazon Research, 2021
Slides
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Generative Neural Scene Representations for 3D-Aware Image Synthesis
Massachusetts Institute of Technology, 2021
Slides / Recording
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KI Forschung und 3D Deep Learning
Frauenhofer IAO event 100 KI Talents, 2020
Slides / Recording
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3D Deep Learning in Function Space
NVIDIA. NVIDIA GPU Technology Conference (GTC), 2020
Slides / Recording

Homepage Template

Feel free to use this website as a template! It is fully responsive and very easy to use and maintain as it uses a python script that crawls your bib files to automatically add the papers and talks. If you find it helpful, please add a link to my website - I will also add a link to yours (if you want). Checkout the github repository for instructions on how to use it.