Optimization Algorithm for Point Cloud Quality Enhancement Based on Statistical Filtering

Abstract

In the process of acquiring point cloud data by a 3D laser scanner, some problems, such as outliers, mixed points, and holes, may be caused in the target point cloud due to the external environment, the discreteness of the laser beam, and the occlusion of objects. In this paper, a point cloud quality optimization and enhancement algorithm is designed. A self-adaptive octree is established to rasterize the point cloud and calculate the density of each grid, combing with the statistical filtering to remove outliers from the point cloud data. Then, a plane projection method is used for removing the confounding points from the point cloud data. Finally, the point cloud is triangulated and a priority value is set, and then, points are preferentially inserted where the priority value is the largest to repair the holes. Experiments show that while removing outliers and confounding points, the detailed features of the point cloud can be maintained, holes are effectively filled, and the quality of the point cloud is effectively improved.