基于水平集的扫描点云简化算法及在人体模型上的应用

简化是计算机图形学的一个重要课题.针对真实性建模技术在图形领域的迅速发展和三维扫描设备的广泛应用,提出一种基于水平集的扫描点简化算法,对高密度的扫描点模型作简化处理,并能解决扫描点模型中普遍存在的重影与空洞问题.该方法具有自动排序特点,并且使筒化结果保留原扫描点模型按层划分的特性.通过指定不同的简化半径,该算法自动生成多分辨率的层次细节模型.实验结果显示,新方法在高简化精度下具有好的保形性,所得模型的误差优于角度简化结果.该方法对于高曲率细节显示的特殊需求可作自适应拓展.简化所得的点模型可通过多边形或曲线、曲面重建.三维人体模型重建工作是服装仿真、计算机动画等方面研究的基础性工作.经简化方法处理所得不同精度的人体模型能作为后续应用的基础.

Level-Set Based Point Cloud Simplification Algorithm and Its Application to Human Body Model

Simplification is an important task in computer graphics (CG). In the light of the rapid development of reality modeling technology in CG and the 3D scanner's wide application in this field, a level-set based simplification algorithm is presented, which can process high dense scan point cloud. The algorithm can solve the multi-contour and hole problems that are ubiquitous in the original point cloud. It has the advantage of ordering points automatically and holding result point cloud to keep layer-based pro...