Study of point cloud data reduction algorithm integrating space partition and curvature
空间分割与曲率相融合的点云精简算法研究

There are huge amounts of redundant data in point cloud data obtained by non-contact scanning. In order to realize model reconstruction effectively, point cloud data reduction is an indispensable means of pre-processing means. This paper presented an approach of point cloud data reduction based on space partition and curvature. Through some key technologies, such as K-neighborhood search, second surface fitting, curvature estimation, and data partition by of controllable curvature threshold, it applied the different reduction algorithms in different regions of the same point cloud data, meanwhile, achieved realizable reduction proportions. So, the algorithm can ensure reduction efficiency and retain characteristic information of the point cloud data simultaneously.