目的 针对点云数据提出一种基于物体表面变化的自适应切片算法。方法 提出一种快速有效的自适应点云切片算法， 该算法基于空间极坐标系划分建立切片层和离散点之间的拓扑关联信息， 通过反距离加权法构造平面散乱点列， 使用变化率平均法来插补切片。结果 算法快速准确、 稳定可靠，能够在物体高变化区域获得更多的切片。结论 基于物体表面变化的自适应切片算法是一种合理有效的点云切片算法。

Targeting at point cloud data, an adaptive slicing algorithm based on change of object surface was proposed. A speedy and effective adaptive cloud point slicing methodology was proposed, which established the topological interaction between slices and between cloud points based on space polar coordinate. Planar scattered point column was built through inverse distance weighting and slices were built through changing rate average. This methodology was speedy, accurate, steady and reliable, and could obtain more slices in fast-changed area. In conclusion, the adaptive cloud point slicing algorithm based on change of object surface is a reasonable and efficient algorithm.