Surface reconstruction method for particle-based fluids using discrete indicator functions

Particle-based methods are practical for computing level-sets that represent liquid interfaces. However, these methods are computationally expensive to reconstruct the liquid surface when the number of particles increases considerably due to the massive amount of particle approximations. This paper introduces two simple and efficient surface reconstruction methods for particle-based fluids based on discrete indicator functions (DIFs). The first approach provides fast level-set approximation using a DIF defined by counting particles inside grid cells. The second approach generates a high-quality liquid surface using a DIF obtained by the particle distribution inside grid cells. Our DIF-based methods are fast, easy to code, and can be adapted straightforwardly in particle-based fluid solvers, even implemented in GPU. Moreover, we show the effectiveness of our approaches through experiments against prior surface reconstruction methods.