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http://dx.doi.org/10.15701/kcgs.2019.25.3.105

Quad Tree Based 2D Smoke Super-resolution with CNN  

Hong, Byeongsun (Korea University)
Park, Jihyeok (Korea University)
Choi, Myungjin (Korea University)
Kim, Changhun (Korea University)
Publication Information
Journal of the Korea Computer Graphics Society / v.25, no.3, 2019 , pp. 105-113 More about this Journal
Abstract
Physically-based fluid simulation takes a lot of time for high resolution. To solve this problem, there are studies that make up the limitation of low resolution fluid simulation by using deep running. Among them, Super-resolution, which converts low-resolution simulation data to high resolution is under way. However, traditional techniques require to the entire space where there are no density data, so there are problems that are inefficient in terms of the full simulation speed and that cannot be computed with the lack of GPU memory as input resolution increases. In this paper, we propose a new method that divides and classifies 2D smoke simulation data into the space using the quad tree, one of the spatial partitioning methods, and performs Super-resolution only required space. This technique accelerates the simulation speed by computing only necessary space. It also processes the divided input data, which can solve GPU memory problems.
Keywords
Quad Tree; Super-resolution; Acceleration; Smoke Simulation;
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