论文标题
标量：现实世界标量传输流量的大规模体积数据集用于计算机动画和机器学习
ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning
论文作者
论文摘要
在本文中，我们提出标量，这是真实烟羽的重建的第一个大规模数据集。我们还为来自少数视频流的准确基于物理的重建提供了一个框架。我们算法的中央组成部分是对看不见的流入区域和有效的正则化方案的新估计。我们的数据集包括大量复杂和自然浮力驱动的流动。流向湍流的过渡并包含可观察到的标量传输过程。因此，标量数据集针对计算机图形，视觉和学习应用程序量身定制。已发布的数据集将包含速度和密度，输入图像序列的体积重构，以及校准数据，代码和指令如何重新创建商品硬件捕获设置。我们进一步证明了许多潜在的应用领域之一：首次感知评估研究，该研究表明，被捕获的流的复杂性需要为常规求解器提供巨大的模拟分辨率，以便重新创建捕获数据中包含的至少部分自然复杂性。
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.
