Adaptive grid refinement using view-dependent octree for smoke simulation

Abstract

Computational cost is one of the major problems in animating smoke. Recently, adaptive grid refinement using octree structure has been proposed which is a successful method for reducing the computational cost of detail-preserving fluid simulation. Although octree grid is optimized for details, viewing angle is not addressed. Smoke distant from the viewing screen or beyond the viewing frustum, which usually has less visual attention and is unnecessary for high-detail simulation, can be optimized for speed. However, applying such view-dependent optimization to the octree grid directly might cause animation artifacts and loss in natural fluid behaviors. In this thesis, we have presented a method for view-dependent adaptive grid refinement, extending the traditional octree grid by considering the viewing frustum, as well as the variation in fluid quantities as criteria for grid refinement. In our method, refinement conditions with adaptive thresholds are proposed to optimize the grid for both viewing angle and details. The proposed method preserves visual details and fluid behaviors which allows high-detail smoke animations with a relatively less computational cost. In addition, particles, which are more flexible to conform to obstacle-fluid boundaries, are integrated to enhance animation and reduce artifacts caused by dynamic refinements. Overall, the method provides a flexible framework for optimization that can be applied for various fluid simulations.