GPU-based Grass Simulation with Accurate Blade Reconstruction

Abstract

Grass is a very important element of nature and it could almost be found in every natural scene. Thus grass modeling, rendering as well as simulation becomes an important task for virtual scene creation. Existing manual grass modeling and reconstruction methods have researched on generate or reconstructing plants. However, these methods do not achieve a good result for grass blades for their extremely thin shape and almost invariant surface color. Besides, current simulation and rendering methods for grasses suffer from efficiency and computation complexity problems. This paper introduces a framework that reconstructs the grass blade model from the color-enhanced depth map, simplifies the grass blade model and achieves extremely large scale grassland simulation with individual grass blade response. Our method starts with reconstructing the grass blade model. We use color information to guide the refinement of captured depth maps from cameras based on an autoregressive model. After refinement, a high-quality depth map is used to reconstruct thin blade models, which cannot be well handled by multi-view stereo methods. Then we introduce a blade simplification method according to each vertex’s movement similarity. This method takes both geometry and movement characteristics of grass into account when simplifying blade mesh. In addition, we introduce a simulation technique for extremely large grassland that achieve tile management on GPU and allow individual response for each grass blade. Our method excels at reconstructing slender grass blades as well as other similar plants, and realistic dynamic simulation for large scale grassland.