点云模型适应性上采样算法

摘要

针对点云模型采样密度的不足,提出一种新的适应性上采样算法.算法首先采用均匀栅格法建立点云模型的拓扑关系,提高数据点K-邻域的查找效率,利用协方差矩阵求取点云模型中数据点的法向量,并用法向传播算法进行法向重定向,然后检测点云模型中采样点密度不足的区域,在采样密度不足区域的点的切向矩形平面内适应性均匀采样,并把这些采样点几乎垂直投影到点云模型所在的原始曲面上,由此得到的模型即为上采样模型.该算法得到的上采样模型可以较好地补充点云模型的细节信息,能够满足点云模型的绘制和后续几何处理的需求.%For insufficient sampling density of point nloud model, this paper proposed a new adaptive up-sampling algorithm. Firstly, it used uniform grids method to represent the spatial topology relationship of point cloud in order to improve the efficiency of finding the K-nearest neighbors for each data point, and estimated normal vectors of data points by constructing co-variance matrix, and computed a consistent orientation of the normal vectors using normal propagation algorithm. Then, it detected these regions with insufficient sampling density dynamically, and adaptive resampled points uniformly in the tangent plane of bounding rectangle originated at this point of insufficient sampling density, and the re-samples were projected onto the underlying surface of point cloud model to achieve the final up-sampling result using the almost orthogonal projection. The up-sampling models could preferably supplement these little detail information of point cloud, and satisfy the needs of rendering point cloud model and subsequent geometric processing.