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In this paper, we present ScalarFlow, a first large-scale data set ofreconstructions of real-world smoke plumes. We additionally propose a frameworkfor accurate physics-based reconstructions from a small number of videostreams. Central components of our algorithm are a novel estimation of unseeninflow regions and an efficient regularization scheme. Our data set includes alarge number of complex and natural buoyancy-driven flows. The flows transitionto turbulent flows and contain observable scalar transport processes. As such,the ScalarFlow data set is tailored towards computer graphics, vision, andlearning applications. The published data set will contain volumetricreconstructions of velocity and density, input image sequences, together withcalibration data, code, and instructions how to recreate the commodity hardwarecapture setup. We further demonstrate one of the many potential applicationareas: a first perceptual evaluation study, which reveals that the complexityof the captured flows requires a huge simulation resolution for regular solversin order to recreate at least parts of the natural complexity contained in thecaptured data.

ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning