Songyou Peng - Homepage

Songyou Peng (彭崧猷)

I am a PhD student at ETH Zurich and Max Planck Institute for Intelligent Systems as part of Max Planck ETH Center for Learning Systems. I am co-supervised by Marc Pollefeys and Andreas Geiger.

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.

News

08/2020: Start my time at Autonomous Vision Group (AVG) at MPI Tubingen.
07/2020: Our paper Convolutional Occupancy Networks is accepted to ECCV 2020 as spotlight (top 5%)!
09/2019: Start PhD journey at Max Planck ETH Center for Learning Systems!
07/2019: Our paper Calibration Wizard is accepted to ICCV 2019 as oral presentation.
06/2019: One paper is accepted to TPAMI 2019!

Research

Shape As Points: A Differentiable Poisson Solver
Songyou Peng, Chiyu "Max" Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, Andreas Geiger
arXiv, 2021
paper

An interpretable and lightweight shape representation that yields HQ watertight meshes at low inference times.

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction
Michael Oechsle, Songyou Peng, Andreas Geiger
arXiv, 2021
paper

Our method enables to reconstruct accurate surfaces without input masks.

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
Christian Reiser, Songyou Peng, Yiyi Liao, Andreas Geiger
arXiv, 2021
paper

Significant speed-ups for NeRF are possible by utilizing thousands of tiny MLPs.

Dynamic Plane Convolutional Occupancy Networks
Stefan Lionar*, Daniil Emtsev*, Dusan Svilarkovic*, Songyou Peng
Winter Conference on Applications of Computer Vision (WACV), 2021
(* equal contribution)
paper | code | video

A student project of 3D Vision course at ETH Zurich where I served as the advisor.

Convolutional Occupancy Networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, Andreas Geiger
European Conference on Computer Vision (ECCV), 2020 (Spotlight, top 5%)
paper | project page | video | teaser video | code

A flexible implicit representation for accurate large-scale 3D reconstruction.

DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, Zhaopeng Cui
IEEE 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.

Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
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.

Photometric Depth Super-Resolution
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.

	PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship 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.
	Give Me One Portrait Image, I Will Tell You Your Emotion and Personality 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.
	Depth Super-Resolution Meets Uncalibrated Photometric Stereo 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.
	High Quality Shape from a RGB-D Camera using Photometric Stereo Songyou Peng M.Sc. Thesis, Techinical University of Munich Supervisor: Yvain Queau and Daniel Cremers thesis \| bibtex \| poster