Adams, Bart × Pauly, Mark Keiser, Richard Guibas, Leonidas #
Association for Computing Machinery
ACM transactions on graphics vol:26 issue:3
We present novel adaptive sampling algorithms for particle-based
ﬂuid simulation. We introduce a sampling condition based on geometric local feature size that allows focusing computational resources in geometrically complex regions, while reducing the number of particles deep inside the ﬂuid or near thick ﬂat surfaces. Further performance gains are achieved by varying the sampling density according to visual importance. In addition, we propose a novel
ﬂuid surface deﬁnition based on approximate particle–to–surface distances that are carried along with the particles and updated appropriately. The resulting surface reconstruction method has several advantages over existing methods, including stability under
particle resampling and suitability for representing smooth ﬂat surfaces. We demonstrate how our adaptive sampling and distance-based surface reconstruction algorithms lead to signiﬁcant improvements in time and memory as compared to single resolution particle simulations, without signiﬁcantly affecting the ﬂuid ﬂow behavior.