• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.11-19
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• 2017

We propose a new pipeline of fluid surface reconstruction that incorporates hybrid SDF(signed distance fields) and adaptive fluid surface techniques to finely reconstruct liquid-solid mixed surfaces. Previous particle-based fluid simulation suffer from a noisy surface problem when the particles are distributed irregularly. If a smoothing scheme is applied to reduce the problem, sharp and detailed features can be lost by over-smoothing artifacts. Our method constructs a hybrid SDF by combining signed distance values from the solid and liquid parts of the object. We also proposed a method of adaptively reconstructing the surface of the fluid to further improve the overall efficiency. This not only shows the detailed surface of the solid and liquid parts, but also the detail of the solid surface and the smooth fluid surface when both materials are mixed. We introduce the concept of guiding shape and propose a method to get signed distance value quickly. In addition, the hybrid SDF and mesh reconstruction techniques are integrated in the adaptive framework. As a result, our method improves the overall efficiency of the pipeline to restore fluid surfaces.