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.

本文介绍了 ScalarFlow，这是第一个大规模真实烟雾柱重建数据集。我们提出了一个准确的物理基础重建框架，其中包括估计难以观测的流入区域和一种有效的规则化方法。我们的数据集包含复杂和自然的自然驱动流，并演变至湍流，并包含可观测的标量传输过程。因此，ScalarFlow 数据集适用于计算机图形，视觉和学习应用。我们进一步展示了许多潜在应用领域之一：第一种感知评估研究，这表明捕获流的复杂性要求规则求解器进行巨大的仿真分辨率，以至少重新创建包含在捕获数据中的自然复杂性的一部分。