海量测量数据简化技术研究

随着坐标测量设备的发展，快速方便地获取包含被测物体更多细节的海量数据成为可能，但由于其数据量非常庞大，会严重影响模型重建算法的效率。文中提出了3种海量数据简化准则：简化后数据集中点的个数、数据集中点的密度阈值及删除一点引起的法向误差的阈值。根据这些准则，分别给出了相应的数据简化算法。该算法的核心是用Riemann图建立散乱测点间的领接关系，在此基础上进行Riemann图的最优遍历并计算测点处的最小二乘拟合平面，从而近似计算删除一点引起的误差。文中提出的对一海量数据进行空间划分的算法，提高了数据简化的效率，应用实例表明本文提出的算法达到了预期的效果。

Research on Cloud Data Simplification

With the progress of measuring equipment, cloud data that contain more details of the object can be obtained conveniently. On the other hand, large quantity of sampled points bring difficulties to model reconstruction. To simplify the cloud data, we put forward three kinds of criteria, namely, the point number after simplification, the density of data set and the error in normal direction caused by deleting a point. According to these criteria, we present corresponding algorithms to automatically reduce the number of cloud points. The key steps in the algorithms are establishing Riemann graph to represent the neighborhood relationship among sample points, traversing the Riemann graph in optimized way and calculating the least square fitting planes at the sample points. In order to improve the efficiency of algorithms, a spatial partitioning scheme is put forward. Practical examples in the paper show that the proposed algorithms are satisfying.