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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.15786 |
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| _version_ | 1866911013237948416 |
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| author | Chen, Peter Yichen Guo, Minghao Pfister, Hanspeter Lin, Ming Freeman, William Huang, Qixing Shen, Han-Wei Matusik, Wojciech |
| author_facet | Chen, Peter Yichen Guo, Minghao Pfister, Hanspeter Lin, Ming Freeman, William Huang, Qixing Shen, Han-Wei Matusik, Wojciech |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15786 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graphics4Science: Computer Graphics for Scientific Impacts Chen, Peter Yichen Guo, Minghao Pfister, Hanspeter Lin, Ming Freeman, William Huang, Qixing Shen, Han-Wei Matusik, Wojciech Graphics Artificial Intelligence Machine Learning Computational Physics Optics 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 |
| title | Graphics4Science: Computer Graphics for Scientific Impacts |
| topic | Graphics Artificial Intelligence Machine Learning Computational Physics Optics |
| url | https://arxiv.org/abs/2506.15786 |