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ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning | | Marie-Lena Eckert
; Kiwon Um
; Nils Thuerey
; | | Date: |
20 Nov 2020 | | Abstract: | In this paper, we present ScalarFlow, a first large-scale data set of
reconstructions of real-world smoke plumes. We additionally propose a framework
for accurate physics-based reconstructions from a small number of video
streams. Central components of our algorithm are a novel estimation of unseen
inflow regions and an efficient regularization scheme. Our data set includes a
large number of complex and natural buoyancy-driven flows. The flows transition
to turbulent flows and contain observable scalar transport processes. As such,
the ScalarFlow data set is tailored towards computer graphics, vision, and
learning applications. The published data set will contain volumetric
reconstructions of velocity and density, input image sequences, together with
calibration data, code, and instructions how to recreate the commodity hardware
capture setup. We further demonstrate one of the many potential application
areas: a first perceptual evaluation study, which reveals that the complexity
of the captured flows requires a huge simulation resolution for regular solvers
in order to recreate at least parts of the natural complexity contained in the
captured data. | | Source: | arXiv, 2011.10284 | | Services: | Forum | Review | PDF | Favorites | |
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