Browsing by Author "Ando, Ryoichi"

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    GPU Smoke Simulation on Compressed DCT Space
    (The Eurographics Association, 2019) Ishida, Daichi; Ando, Ryoichi; Morishima, Shigeo; Cignoni, Paolo and Miguel, Eder
    This paper presents a novel GPU-based algorithm for smoke animation. Our primary contribution is the use of Discrete Cosine Transform (DCT) compressed space for efficient simulation. We show that our method runs an order of magnitude faster than a CPU implementation while retaining visual details with a smaller memory usage. The key component of our method is an on-the-fly compression and expansion of velocity, pressure and density fields. Whenever these physical quantities are requested during a simulation, we perform data expansion and compression only where necessary in a loop. As a consequence, our simulation allows us to simulate a large domain without actually allocating full memory space for it. We show that albeit our method comes with some extra cost for DCT manipulations, such cost can be minimized with the aid of a devised shared memory usage.
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    Tiled Characteristic Maps for Tracking Detailed Liquid Surfaces
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Narita, Fumiya; Ando, Ryoichi; Dominik L. Michels; Soeren Pirk
    We introduce tiled characteristic maps for level set method that accurately preserves both thin sheets and sharp edges over a long period of time. Instead of resorting to high-order differential schemes, we utilize the characteristics mapping method to minimize numerical diffusion induced by advection. We find that although a single characteristic map could be used to better preserve detailed geometry, it suffers from frequent global re-initialization due to the strong distortions that are locally generated. We show that when multiple localized tiled characteristic maps are used, this limitation is constrained only within tiles; enabling long-term preservation of detailed structures where little distortion is observed. When applied to liquid simulation, we demonstrate that at a reasonably amount of added computational cost, our method retains small-scale high-fidelity (e.g., splashes and waves) that is quickly smeared out or deleted with purely grid-based or particle level set methods.