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.

中文翻译：

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ScalarFlow：用于计算机动画和机器学习的现实世界标量传输流的大规模体积数据集

在本文中，我们介绍了ScalarFlow，这是真实世界烟羽重构的第一个大规模数据集。我们还提出了一个框架，可从少量视频流中进行基于物理的精确重建。我们算法的主要组成部分是对看不见的流入区域的新颖估计和有效的正则化方案。我们的数据集包括大量复杂的自然浮力驱动的流量。流量转换为湍流，并包含可观察的标量传输过程。因此，ScalarFlow数据集是针对计算机图形，视觉和学习应用程序量身定制的。已发布的数据集将包含速度和密度的体积重建，输入图像序列以及校准数据，代码以及如何重新创建商品硬件捕获设置的说明。