Graphics4Science: Computer Graphics for Scientific Impacts
It targets the graphics and scientific communities to foster collaboration on high-impact problems, but it is incremental as it builds on existing interdisciplinary work.
This paper explores the relationship between computer graphics and science, aiming to reframe graphics as a modeling language to address scientific challenges, especially in data-scarce settings, by bridging vocabulary gaps between the two communities.
Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields, especially in data-scarce settings. To that end, we aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities. Designed for both newcomers and experts, Graphics4Science invites the graphics community to engage with science, tackle high-impact problems where graphics expertise can make a difference, and contribute to the future of scientific discovery. Additional details are available on the course website: https://graphics4science.github.io