Papers › Global Transport for Fluid Reconstruction with Learned Self-Supervision

Global Transport for Fluid Reconstruction with Learned Self-Supervision

13 Apr 2021CVPR 2021 1arXiv:2104.06031archive 2025-07-28

Erik Franz, Barbara Solenthaler, Nils Thuerey

We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. Instead of obtaining the space-time function of the observations, we reconstruct its motion based on a single initial state. In addition we introduce a learned self-supervision that constrains observations from unseen angles. These visual constraints are coupled via the transport constraints and a differentiable rendering step to arrive at a robust end-to-end reconstruction algorithm. This makes the reconstruction of highly realistic flow motions possible, even from only a single input view. We show with a variety of synthetic and real flows that the proposed global reconstruction of the transport process yields an improved reconstruction of the fluid motion.

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