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I am a PhD student at ETH Zurich and Max Planck Institute for Intelligent Systems under Max Planck ETH Center for Learning Systems & ELLIS. I am co-supervised by Marc Pollefeys and Andreas Geiger. During my PhD I was a research intern at Google Research working with Tom Funkhouser, and Meta Reality Labs Research, hosted by Michael Zollhoefer. I completed an Erasmus Mundus Masters in Computer Vision and Robotics (VIBOT) with distinction. During the master, I was fortunate to be supervised by Daniel Cremers at Technical University of Munich for my thesis and work with Peter Sturm at INRIA for two summers. Before this, I obtained a Bachelors in Automation at Xi'an Jiaotong University. I have also spent some time at ADSC and A*STAR in Singapore. |
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Shengqu Cai, Eric R. Chan, Songyou Peng, Mohamad Shahbazi, Anton Obukhov, Luc Van Gool, Gordon Wetzstein International Conference on Computer Vision (ICCV), 2023 paper | project page A diffusion-model based unsupervised framework capable of synthesizing novel views depicting a long camera trajectory. |
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 OpenScene: 3D Scene Understanding with Open Vocabularies
Songyou Peng, Kyle Genova, Chiyu "Max" Jiang, Andrea Tagliasacchi, Marc Pollefeys, Thomas Funkhouser Conference on Computer Vision and Pattern Recognition (CVPR), 2023 paper | project page | video | code Zero-shot approach for novel 3D scene understanding tasks with open-vocabulary queries. |
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 : A Unified Framework for Surface Reconstruction
Zehao Yu, Anpei Chen, Bozidar Antic, Songyou Peng, Apratim Bhattacharyya, Michael Niemeyer, Siyu Tang, Torsten Sattler, Andreas Geiger Open Source Project, 2023 project page | code We provide a unified framework and benchmark for neural implicit surface reconstruction. |
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Lixiang Lin, Songyou Peng, Qijun Gan, Jianke Zhu arXiv, 2022 paper | project page Shape As Points (SAP) for fast human body reconstruction. |
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Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, Andreas Geiger Advances in Neural Information Processing Systems (NeurIPS), 2022 paper | project page Monocular depth and normal cues significantly boost the performance of neural implicit surface reconstruction methods. |
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 NICE-SLAM: Neural Implicit Scalable Encoding for SLAM
Zihan Zhu*, Songyou Peng*, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, Marc Pollefeys Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (* equal contribution) paper | project page | video | code A neural implicit-based RGB-D SLAM that can be applied to large-scale scenes. |
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Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger Advances in Neural Information Processing Systems (NeurIPS), 2021 (Oral, top 0.6%) paper | project page | video (6 min) | video (12 min) | podcast | code An interpretable hybird shape representation that yields HQ watertight meshes at low inference times. |
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Michael Oechsle, Songyou Peng, Andreas Geiger International Conference on Computer Vision (ICCV), 2021 (Oral, top 3%) paper | project page | video | teaser video | code Our method enables to reconstruct accurate surfaces without input masks. |
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Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger International Conference on Computer Vision (ICCV), 2021 paper | project page | blog | video | teaser video | code Over 2000x speed-ups for NeRF are possible by utilizing thousands of tiny MLPs. |
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Stefan Lionar*, Daniil Emtsev*, Dusan Svilarkovic*, Songyou Peng Winter Conference on Applications of Computer Vision (WACV), 2021 (* equal contribution) paper | video | code A student project of 3D Vision course at ETH Zurich where I served as the advisor. |
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Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger European Conference on Computer Vision (ECCV), 2020 (Spotlight, top 5%) paper | project page | blog | video | teaser video | code A flexible implicit representation for accurate large-scale 3D reconstruction. |
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Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui Conference on Computer Vision and Pattern Recognition (CVPR), 2020 paper | project page | teaser video | poster | code A differentiable renderer for deep implicit signed distance functions. |
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Songyou Peng and Peter Sturm International Conference on Computer Vision (ICCV), 2019 (Oral, top 4.6%) paper | video | poster | code A novel system that interactively guides a user to take optimal calibration images. |
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Bjoern Haefner*, Songyou Peng*, Alok Verma*, Yvain Queau, Daniel Cremers IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019 (* equal contribution) paper | project page Recover high-resolution depth maps with fine geometric details using photometric techniques. |
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Le Zhang, Songyou Peng, Stefan Winkler IEEE Transactions on Affective Computing (TAFFC), 2019. In press. paper | code A journal extension of our ACM MM 2018 paper. |
 
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Songyou Peng, Le Zhang, Stefan Winkler, Marianne Winslett ACM International Conference on Multimedia (ACM MM), 2018 paper | slides | code Technical Demo. A deep Siamese-like network is introduced to predict one's Big-Five personality and arousal-valence emotion from one portrait photo. |
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Songyou Peng, Bjoern Haefner, Yvain Queau, Daniel Cremers International Conference on Computer Vision (ICCV) Workshops, 2017 paper | slides | code & data A novel depth super-resolution approach for RGB-D sensors is presented. This paper a part of my master thesis, and subsumed by our TPAMI paper. |
 
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Teaching Assistant (Lead), 3D Vision, Spring 2023
Teaching Assistant, Computer Vision, Fall 2022 Teaching Assistant (Lead), 3D Vision, Spring 2022 Teaching Assistant, Deep Learning for Computer Vision: Seminal Work, Spring 2022 Teaching Assistant, 3D Vision, Spring 2020 Teaching Assistant, Deep Learning for Computer Vision: Seminal Work, Spring 2020 |
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Teaching Assistant, Deep Learning, Winter 2020/2021
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