面向三角网格的自适应细分

Adaptive Refinement Scheme for Triangular Meshes

细分曲面存在的一个问题是随着细分次数的增多,网格的面片数迅速增长,巨大的数据量使得细分后的模难以进行其它处理。针对这个问题,该文利用控制点的局部信息提出了一种基于Loop模式的自适应细分算法,利用该算法可避免在相对光滑处再细分,与正常细分相比,既大大减少了数据量,提高了模型的处理速度,又达到了对模型进行细分的目的。

One problem in subdivision surfaces is the number of facets grows exponentially with the level of subdivision. Subdivision schemes are cost intensive at higher levels of subdivision. This paper proposes an adaptive subdivision scheme for subdivision surfaces based on triangular meshes and exploits the local smoothness information of a surface for adaptive refinement of a model. With this approach, it can avoid unnecessary subdivision in relative smooth areas and represent surfaces with lower cost when compared with those obtained by uniform subdivision schemes. This paper compares the methods for various 3D graphic meshes and presents the results.