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Gradient 𝚫 Spaces Research Group

Department of Civil and Environmental Engineering

Stanford University

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About Us

Welcome to the Gradient 𝚫 Spaces Research Group. The group belongs to the Civil and Environmental Engineering Department, Stanford University, under the Schools of Engineering and Sustainability. Our research and educational activities focus on developing quantitative and data-driven methods that learn from real-world visual data to generate, predict, and simulate new or renewed built environments that place the human in the center. Our mission is to create sustainable, inclusive, and adaptive built environments that can support our current and future physical and digital needs. Of particular interest is the creation of spaces that blend from the 100% physical (real reality) to the 100% digital (virtual reality) and anything in between, with the use of mixed reality and multi-level design (i.e., of buildings, processes, UXs, etc.). We believe that by cross-pollinating the two domains, we can achieve higher immersion and view these spaces as a step toward more equitable living conditions. Hence, we aim for developing methods that work in real-world settings on a global scale. To achieve the above, we are building a cross- and inter- disciplinary team that is diverse and well-rounded. Most importantly, we are driven by curiosity and learning, and so does everything we do.

To learn more about this vision, you can read this short story to illustrate this future and the impact on designers: 
A Day in the Life of an Architect in the Gradient World

News

New Spring Course!

CEE 342: Designing for Gradient Spaces

We are teaching a new course on connecting physical and digital experiences.

Want to learn more?

Mark your calendars!

Upcoming Events

We are organizing three workshops and challenges in the next coming months.

Want to learn more?

Research Highlights

Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
Liyuan Zhu, Shengyu Huang, Konrad Schindler, Iro Armeni
Computer Vision and Pattern Recognition (CVPR) 2024
[pdf] [webpage] [code]

Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature
Shengze Jin, Daniel Barath, Marc Pollefeys, Iro Armeni
3DV 2024
[pdf]

SGAligner: 3D Scene Alignment with Scene Graphs
Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
ICCV 2023 Conference
[pdf]   [website]   [code]   [benchmark]