Novel fluid detail enhancement based on multi-layer depth regression analysis and FLIP fluid simulation

摘要

In this paper, we propose a novel integrated method for effective modeling and realistic enhancement of scale-sensitive fluid simulation details. The core of our method is the organic of multi-layer depth image regression analysis and fluid implicit particle fluid simulation of which the regression analysis induces the criterion where the fluid details should be produced. First, we capture the depth buffer of the fluid surface dynamically from the top of scene. Second, we employ depth peeling technique to decompose the target fluid volume into multiple depth layers and conduct time-space analysis over surface layers. Third, we propose a logistic regression-based model to rigorously pinpoint the complex interacting regions, wherein multiple detail-relevant factors are taken into account based on the captured multiple depth layers. Finally, details are enhanced by animating extra diffuse materials and augmenting the air-fluid mixing phenomenon. It is evident that, with depth peeling technology, we can afford rigorous analysis not only across surface layers at different fluid depth but along the depth direction as well. After integrating the analysis results from these two sources, we are capable of performing detail enhancement both on the fluid surface and inside the fluid to obtain a great visual effect, even when large occlusion exists. Directly benefiting from the flexibility of image-space-dominant processing, our unified framework can be entirely implemented on graphics processing units and thus achieves interactive performance. For various fluid phenomena with different diffuse materials (e.g., spray, foam, and bubble), comprehensive experiments and evaluations have demonstrated its superiority in high-fidelity fluid detail enhancement and its interaction with surrounding environment.