基于三角形不规则网模型的快速体素化方法

为了改善在大数据量时体素化效率不高的缺点，针对三角形不规则网（TIN）模型的三角网特性，提出了一种快速简单的体素化算法。首先通过细划三角面片，将面体素化转换为简单的点体素化生成体表面模型，然后利用深度缓存原理快速寻找初始种子体素进行体内填充。实验结果表明，对于精细复杂的大规模TIN模型，算法能确实有效地生成逼近原模型的26-连通的体素模型，且具有高效的时间效率。

Fast voxelization based on triangulated irregular network model

In order to improve the voxelization efficiency of large data, a fast and simple voxelization algorithm was proposed for the Triangulated Irregular Network (TIN) model. The surface voxelization was realized by dividing triangular facets, which could convert surface voxelization to points voxelization. Then, the solid voxelization was realized by using the theory of depth buffer to quickly search initial seed. The experimental results show that for the complex and precise TIN model, a voxel model of 26-connected which is approached to original model can be efficiently generated, and it has a high time efficiency.