3rd Workshop on Scalable 3D Scene Generation and Geometric Scene Understanding

Angela Dai is an Associate Professor at the Technical University of Munich where she leads the 3D AI Lab. Angela's research aims to enable machines to understand, model, and generate real-world 3D environments. She focuses on enabling the creation of rich, semantically grounded, and interactable 3D worlds that allow machines not only to perceive physical spaces, but to reason about them and act within them. She received her PhD in computer science from Stanford in 2018, advised by Pat Hanrahan, and her BSE in computer science from Princeton in 2013. Her research has been recognized through an ECVA Young Researcher Award, ERC Starting Grant, Eurographics Young Researcher Award, German Pattern Recognition Award, Google Research Scholar Award, and an ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention. She has also served as Program Chair for Eurographics 2025 and CVPR 2026.

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Andreas Geiger is a Professor and is heading the Autonomous Vision Group (AVG) at the University of Tübingen located in Tübingen, Germany at the heart of CyberValley. He is the head of the Department of Computer Science, a core faculty member of the Tübingen AI Center, PI in the cluster of excellence ML in Science and the CRC Robust Vision. He is also an ELLIS fellow and coordinator of the ELLIS PhD program. His research group is developing machine learning models for computer vision, natural language and robotics with applications in self-driving, VR/AR and scientific document analysis.

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Richard Zhang is a Professor in the School of Computing Science at SFU, and also Vice President of AI and R&D at Augmenta. At Augmenta, he leads the company's effort in developing the most advanced AI models and tools for efficient and sustainable building designs. He directs the GrUVi (Graphics U Vision) Lab, one of the top places in the world to conduct computer graphics and computer vision research. His research is in computer graphics and more broadly, visual computing, with special interests in geometric and generative modeling, shape analysis, 3D vision, spatial AI, geometric deep learning, as well as computational design and fabrication. He has published more than 200 papers on these topics, including 75+ articles in SIGGRAPH (+Asia) and ACM Trans. on Graphics (TOG), the top venue in computer graphics, and he has an Erdös number of 3. His research has been sponsored by Adobe, Autodesk, Boeing, Glodon, Google, and NSERC. He is an IEEE Fellow, hold a Distinguished University Professorship, and is a member of the ACM SIGGRAPH Academy. He was an Amazon Scholar from late 2021 to early 2025.

Ping Tan is a professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). Before joining HKUST, he served as the director of the XR Lab at Alibaba DAMO Academy from 2019 to 2022, an associate professor at Simon Fraser University (SFU) in Canada from 2014 to 2019, and an assistant and associate professor at the National University of Singapore (NUS) from 2007 to 2014. Prof. Tan received his PhD from HKUST in 2007 and his Master's and Bachelor's degrees from Shanghai Jiao Tong University (SJTU) in 2003 and 2000, respectively. He specializes in computer vision, computer graphics, and robotics, with a research focus on 3D vision.

Andrew Davison holds the position of Professor of Robot Vision at the Department of Computing, Imperial College London, and leads the Dyson Robotics Laboratory at Imperial College where they are working on vision and AI technology for next generation home robotics. He is working in computer vision and robotics: specifically his main research has concerned SLAM (Simultaneous Localisation and Mapping) using vision, with a particular emphasis on methods that work in real-time with commodity cameras. He pioneered SLAM with vision from the mid 1990s onwards, and brought the SLAM acronym and methods from robotics to single camera computer vision with the breakthrough MonoSLAM algorithm in 2003 which enabled long-term, drift-free, real-time SLAM from a single camera for the first time, inspiring many researchers and industry developments in robotics and inside-out tracking for VR and AR.

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