In three-dimensional measurement of large-scale parts, such as car bodies and aircraft wings, massive points (up to hundreds of millions) are collected. If all point cloud data is processed, a large amount of computing resources and storage space will be consumed. It is an important task to reduce the burden of processing while maintaining the feature of the point cloud as much as possible. This paper proposes a novel point cloud simplification method using local conditional information that is utilized to evaluate the features of each point in point clouds. The proposed method can reduce the size of point clouds while reserving significant features. Firstly, the original information of each point is evaluated by calculating the curvature of the original point cloud. Secondly, the point with the maximal information is selected and reserved in the target point cloud that will be updated as the alternative point cloud after simplification. The conditional information of each rest point is evaluated by the neighborhood and updated to the information of the current point, which attenuates with the distance from the other reserved points. Finally, the point with the maximal information is reserved iteratively until the number of points reserved satisfies the requirements of simplification according to enterprise needs. Experiments verify the effectiveness and performance of the proposed method. The experimental results indicate that the proposed method performs much better on feature retention than Voxel simplification and Curvature simplification.

No abstract