Songyou Peng is a research scientist at Google DeepMind with a decade of experience building 3D/4D scene representations, pose estimation, and multi-modal spatial reasoning systems. He co-led world-scale 3D scene representation work in collaboration with Google Maps and contributes to Gemini to improve spatial reasoning for multi-modal LLMs. His academic path includes a PhD from ETH Zürich under Marc Pollefeys and Andreas Geiger, earning the ECVA PhD Award 2024, and he has a track record of mentoring PhD and master students. Songyou’s internship at Google produced OpenScene (CVPR 2023), which seeded Google’s Geo Foundational Features effort, and his open-source contributions include practical dataset and evaluation improvements to convolutional occupancy networks. Equally comfortable in research and engineering, he has repeatedly turned novel geometry ideas into scalable systems used in industry projects. Based in San Francisco, he blends deep theoretical grounding with hands-on implementation across academia and leading research labs.
10 years of coding experience
2 years of employment as a software developer
Master of Science (MSc) Computer Vision and Robotics (VIBOT), Master of Science (MSc) Computer Vision and Robotics (VIBOT) at Technical University of Munich
Heriot-Watt University Edinburgh Campus
Master of Science (MSc) Computer Vision and Robotics (VIBOT), Master of Science (MSc) Computer Vision and Robotics (VIBOT) at Université de Bourgogne
Bachelor of Engineering (B.Eng.) Automation, Bachelor of Engineering (B.Eng.) Automation at Xi'an Jiaotong University
Doctor of Philosophy (PhD) Computer Vision and Machine Learning, Doctor of Philosophy (PhD) Computer Vision and Machine Learning at ETH Zürich
Master of Science (MSc) Computer Vision and Robotics (VIBOT), Master of Science (MSc) Computer Vision and Robotics (VIBOT) at Universitat de Girona
Contributions:17 commits, 1 PR, 12 pushes in 2 years
Contributions summary:Songyou focused on modifying and updating the `build_dataset.py` script, likely involved in generating and processing data for training. They added a feature to remove walls during mesh evaluation within `eval.py` and modified `src/common.py` to correct a bug. The commits reflect improvements to the dataset creation process and evaluation procedures, indicating a focus on model training and performance.
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Songyou Peng - Research Scientist at Google DeepMind