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Gradient 𝚫 Spaces Research Group

Department of Civil and Environmental Engineering

Stanford University

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There was a truck in the image. We removed it with AI. Can you spot where it was?
Photo taken in October 2024, Stanford University. Photo Credits: Geoffrey Tuttle

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

Research updates

Two new papers accepted -- one at 3DV 2025 and one at WACV 2025!

Curious which?

Research updates

Two new papers accepted at ECCV 2024!

Curious which?

Martin Juan José Bucher
HAI Graduate Fellow '24-'25

Congratulations to Ph.D. student Martin Juan José Bucher for being a graduate fellow '24-'25 at the center for Human-Centered Artificial Intelligence (HAI).

Research Highlights

3D Reconstruction Output of our LoopSplat SLAM method with trajectory error information

LoopSplat: Loop Closure by Registering 3D Gaussian Splats
Liyuan Zhu, Yue Li, Erik Sandström, Shengyu Huang, Konrad Schindler, Iro Armeni
International Conference on 3D Vision (3DV), 2025
[pdf] [website] [code

3D reconstruction output of our MAP-ADAPT method where different regions are reconstruction in different resolution

MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps
Jianhao Zheng, Daniel Barath, Marc Pollefeys, Iro Armeni
ECCV 2024 Conference
[pdf][website][code]

Overall pipeline of our I-Design method

I-Design: Personalized LLM Interior Designer
Ata Celen, Guo Han, Konrad Schindler, Luc Van Gool, Iro Armeni*, Anton Obukhov*, Xi Wang*
CV4Metaverse, Workshop in ECCV 2024
[pdf] [webpage] [video]