Scattered point cloud simplification based on feature points^*

Point cloud models built with large amounts of data contain more surface detail, but are a severe challenge to the processing and rendering speed of the computer. Point clouds which are too dense can cause the geometric characteristics of the measured entity to be difficult to judge, thus making it very important to simplify the original data. In this paper, the K nearest neighbor of the point cloud is first analyzed, and principal component analysis is used to estimate the normal vector of the scattered point cloud. Then, a method similar to the progressive mesh using a set of tangent planes to approximate the local surface is adopted. Finally, geometric deviation of tangent planes is estimated using the square distance tangent planes, after which the point cloud is simplified. Through verification, it was found that the method has the advantages of having a high reduction rate and the ability to maintain the geometrical features of the point cloud well.