Algorithm of combined reduction based on scattered point cloud

Abstract

In recent years, the 3D laser scanning technology has been becoming a development hot spot in the survey. Due to the lack of monotonic point cloud data compression method, the paper proposes to use the combination algorithm to streamline point cloud data. Curvature is one of the important geometric features of 3D surface. The point cloud data can be reduced through the curvature of the surface, which means that the point cloud data feature can be better reserved. If the amount of point cloud data is very large, curving surface fitting is slower and it consumes more time. Space distance compression algorithm is simple, and the compression is faster, but it is not very good to retain the features. This paper combined the two algorithms together. Firstly, it uses the minimum distance method to reduce the amount of the point cloud data. Then, based on the space bounding box method, the k-nearest neighbors point cloud data are established. After that, the point cloud is fitted to the quadratic surface in the neighborhood, and the average curvature is calculated. After setting the angle threshold, the point cloud data are reduced based on adjacent normal angle values and threshold. Next, through comparing reconstructed triangulation network and modeling with the original model, it demonstrates that the new method of the compression effect is ideal.

Keywords:

• 3D laser scanning technology
• Cloud data
• Distance compression algorithm
• k-nearest neighbors
• Quadratic surface
• Triangulation network

2010 Mathematics Subject Classification:

• 92F05