5 DAILY WACV Sunday “Reconstructing a glacier and a forest are not really that different! I think we can all learn from each other.” The workshop’s call for papers welcomed novel and previously published work and works in progress. There will be no Best Paper award, which Marissa stresses is in line with its focus on community building and encouraging an inclusive, collaborative, and non-competitive atmosphere. Having seen all the papers, were there any surprises in the mix? A diagram illustrating NeuroFluid a differentiable two-stage network consisting of (i) a particle-driven neural renderer, which involves fluid physical properties in the volume rendering function, and (ii) a particle transition model optimized to reduce the differences between the rendered and the observed images. NeuroFluid provides the first solution to unsupervised learning of particle-based fluid dynamics by training these two models jointly. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities. - From "NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields" by Shanyan Guan, Huayu Deng, Yunbo Wang, and Xiaokang Yang 3D Geometry Generation for Scientific Computing
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