Global Transport for Fluid Reconstruction With Learned Self-Supervision

Erik Franz, Barbara Solenthaler, Nils Thuerey; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1632-1642

Abstract


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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Franz_2021_CVPR, author = {Franz, Erik and Solenthaler, Barbara and Thuerey, Nils}, title = {Global Transport for Fluid Reconstruction With Learned Self-Supervision}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {1632-1642} }