Visualizing Climate Change Impacts

Jia-Bin Huang · Intermediate ·📄 Research Papers Explained ·2y ago

About this lesson

ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field Yuan Li, Zhi-Hao Lin, David Forsyth, Jia-Bin Huang, and Shenlong Wang International Conference on Computer Vision (ICCV), 2023 📝 Paper: https://arxiv.org/abs/2211.13226 🌐 Website: https://climatenerf.github.io/ 💻 Code: https://github.com/y-u-a-n-l-i/Climate_NeRF 📄 Abstract: Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes, producing realistic movies of physical phenomena in those scenes. Our application -- Climate NeRF -- allows people to visualize what climate change outcomes will do to them. ClimateNeRF allows us to render realistic weather effects, including smog, snow, and flood. Results can be controlled with physically meaningful variables like water level. Qualitative and quantitative studies show that our simulated results are significantly more realistic than those from SOTA 2D image editing and SOTA 3D NeRF stylization.

Original Description

ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field Yuan Li, Zhi-Hao Lin, David Forsyth, Jia-Bin Huang, and Shenlong Wang International Conference on Computer Vision (ICCV), 2023 📝 Paper: https://arxiv.org/abs/2211.13226 🌐 Website: https://climatenerf.github.io/ 💻 Code: https://github.com/y-u-a-n-l-i/Climate_NeRF 📄 Abstract: Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes, producing realistic movies of physical phenomena in those scenes. Our application -- Climate NeRF -- allows people to visualize what climate change outcomes will do to them. ClimateNeRF allows us to render realistic weather effects, including smog, snow, and flood. Results can be controlled with physically meaningful variables like water level. Qualitative and quantitative studies show that our simulated results are significantly more realistic than those from SOTA 2D image editing and SOTA 3D NeRF stylization.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

I Spent Weeks Looking for a Research Gap Before I Realized I Was Searching the Wrong Way
Learn how to effectively find research gaps by changing your approach, a crucial skill for AI researchers and academics
Medium · AI
ICMI 2026 Reviews [D]
Learn how to interpret ICMI 2026 reviews and improve your paper's acceptance chances
Reddit r/MachineLearning
Workshop submission for main conference paper under review [D]
Learn how to navigate submitting a paper to a non-archival workshop before the final decision of a main conference like ECCV
Reddit r/MachineLearning
Kept context-switching between arxiv, OpenReview, GitHub, and HuggingFace for every paper, so I built this. Chrome extension + website with everything inline, plus citation graph + SPECTER2 neighbors. 3M papers, free, feedback welcome [P]
Streamline your research with a new Chrome extension and website that integrates 3M papers from arxiv, OpenReview, GitHub, and HuggingFace, including citation graphs and SPECTER2 neighbors, and provide feedback to improve it
Reddit r/MachineLearning
Up next
Beyond Big Vendors: ERP Systems Explained #shorts
Digital Transformation with Eric Kimberling
Watch →