Data-driven retrieval of spray details with random forest-based distance

Generating realistic spray details in liquid simulations remains computationally expensive. This paper proposes a data-driven method to simulate high-resolution sprays on low-resolution grids by retrieving details with the most compatible details from a precomputed repository efficiently. We first employ a random forest-based distance (RFD) to measure the similarity of liquid regions. In consideration of spatiotemporal relationships between one liquid region and its neighbors, we define a multinary label for RFD instead of the original binary one. Our improved RFD enables us to retrieve details that fit ground truth the best. To ensure temporal continuity of our result and to generate new details from existing ones, we formulate a series of forests with a training set from different time steps. Then, we synthesize results of each forest according to their distances. Finally, we put the synthesis result in correct positions to generate desired sprays motion. In our method, a state-of-the-art cascade forest is employed for a higher accuracy. Several experiments with various grid resolutions validate our method both in visual effect and computational cost.