GPU-based Adaptive Surface Reconstruction for Real-time SPH Fluids

Abstract

We propose a GPU-based adaptive surface reconstruction algorithm for Smoothed-Particle Hydrodynamics (SPH) fluids. The adaptive surface is reconstructed from 3-level grids as proposed by [Akinci13]. The novel part of our algorithm is a pattern based approach for crack filling, which is recognized as the most challengeable part of building adaptive surfaces. Unlike prior CPU-based approaches [Shu95, Shekhar96, Westermann99, Akinci13] that detect and fill cracks according to some criteria during program running that were slow and unrobust, all the possible crack patterns are analyzed and defined in advance and later, during program running, the cracks are detected and filled according to the patterns. Our approach is thus robust, GPU-friendly, and easy to implement. Results obtained show that our algorithm can produce surface meshes of almost the same quality as those produced by the conventional Marching Cubes method, with significantly reduced computation time and memory usage.