论文标题
CIMS：烟雾模拟的校正插值方法
CIMS: Correction-Interpolation Method for Smoke Simulation
论文作者
论文摘要
在本文中，我们提出了CIMS：一种用于烟雾模拟的新型校正式插值方法。我们方法的基础是首先产生低框架烟雾模拟，然后使用时间插值提高帧速率。但是，低帧速率烟雾模拟不准确，因为它们需要增加时间步长。具有较大时间步长的模拟与以较小的时间步长的原始模拟不同。因此，所提出的方法纠正了使用基于U-NET的DNN模型更接近相应的小时步仿真结果的大型时步仿真结果。为了获得更精确的结果，我们应用了图像域中使用的建模概念，例如光流和感知损失。通过纠正大型时步仿真结果并在它们之间插值，提出的方法可以有效，准确地生成高框架烟雾模拟。我们进行定性和定量分析以确认所提出模型的有效性。我们的分析表明，我们的方法平均将大型时步仿真结果的平均误差降低了80％以上。与以前的基于DNN的方法相比，我们的方法还会产生更接近地面真理的结果。它的准确性平均比以前的作品高2.04倍。此外，提出的校正方法的计算时间几乎不会影响整体计算时间。
In this paper, we propose CIMS: a novel correction-interpolation method for smoke simulation. The basis of our method is to first generate a low frame rate smoke simulation, then increase the frame rate using temporal interpolation. However, low frame rate smoke simulations are inaccurate as they require increasing the time-step. A simulation with a larger time-step produces results different from that of the original simulation with a small time-step. Therefore, the proposed method corrects the large time-step simulation results closer to the corresponding small time-step simulation results using a U-Net-based DNN model. To obtain more precise results, we applied modeling concepts used in the image domain, such as optical flow and perceptual loss. By correcting the large time-step simulation results and interpolating between them, the proposed method can efficiently and accurately generate high frame rate smoke simulations. We conduct qualitative and quantitative analyses to confirm the effectiveness of the proposed model. Our analyses show that our method reduces the mean squared error of large time-step simulation results by more than 80% on average. Our method also produces results closer to the ground truth than the previous DNN-based methods; it is on average 2.04 times more accurate than previous works. In addition, the computation time of the proposed correction method barely affects the overall computation time.
