散乱点云精简的一种改进算法*

非接触式扫描获取的散乱点云数据存在大量冗余，为方便模型重构，点云数据精简是不可或缺的点云预处理步骤，提出一种散乱点云数据精简的改进算法，首先将包围点云数据的最小包围盒划分成若干个子空间，根据每个含有点的子空间，获取K邻域点集的拟合平面，计算K邻域中各点到拟合平面距离的累加和。对各个K邻域的距离累加和升序排列，根据预定精简百分比，将包围盒划分为待保留和待删除两个区域，实现了对同一数据在不同区域采用不同算法，完成不同比例的精简。实例验证表明，该算法在保留几何特征的同时，更能有效地避免“空白区域”，且提高了计算效率。

An Improved Algorithm of Simplifying Scattered Point Cloud Data

There are huge amounts of redundant data in scattered point cloud data obtained by non-contact scanning. In order to realize model reconstruction effectively, scattered point cloud data simplification is an indispensable means of pre-processing means. This paper proposed an improved algorithm for the simplification of scattered point cloud data. First, the bounding box which only contains the point cloud data was divided into several sub-space, and the fitting plane of K-nearest neighbor point was builded by each sub-space that contains points. Second, the distances from each point in the K-nearest neighbor point to the fitting plane were accumulated. Third, all of the accumulated distances were sorted in an ascending order, and the bounding box was divided into two domains as to ready to retain and delete. Different simplification algorithms were applied in different areas of the same point cloud data. Meanwhile, it achieved realizable reduction proportion. So, the algorithm proposed in this paper can not only maintain geometric features of the point cloud data, but also eliminate large hole areas more effectively. Meanwhile, the computational efficiency was increased.