关键词: 三维重建, 点云精简, 分类采样, 特征保留

Abstract: Point clouds reduction is a key step before the triangulation in the progress of 3D-reconstruction which based on images. Generally, the quantity and distributed mass of the point clouds after reducing will contribute to the result of reconstruction directly. This paper keeps the point clouds’ character that the point clouds after reducing by the traditional method distribute adaptively according to curvatures. Considering the disadvantages of traditional method that holes will be found in the flat areas and balanced distribution won’t be guaranteed. The paper gives an improved algorithm to solve the problem. Firstly, it uses bounding box with small grid to simplify the point clouds which will also make it easy for the calculation of point’s local characters. Then it classifies the point clouds and respectively simplifies them based on their normal angle coefficients and the degrees of bending. In the end, the paper gives an easy method to evaluate the result of point clouds after reducing. It has been proved that, the method of this paper can keep the geometrical features well, and can overcome the shortcomings of the traditional method, too.

Key words: 3D-reconstruction, point clouds reduction, classification and respective sampling, feature preserving