基于改进多边滤波的点云三维重建与分析

3D Reconstruction and Analysis of Point Cloud Based on Improved Multilateral Filtering

摘要: 为了提高三维重建过程点云噪声的滤除准确度与重建精确度，提出一种基于尺度分类的点云多边滤波算法，在双边滤波基础上引入尺度分类的自适应参数，将曲率作为第三个滤波影响因子，兼顾滤波过程的噪声滤除与点云平滑。实验结果表明，所提多边滤波算法与双边滤波算法、高斯滤波算法相比，噪声滤除准确度最高，达97.1%。且在重建过程中保证了细节与平滑度，偏离模型真实表面程度最低。

Abstract: In order to improve the filtering accuracy and reconstruction accuracy of point cloud noise in 3D reconstruction process, a multi-lateral filtering algorithm of point cloud based on scale classification was proposed. The adaptive parameters of scale classification were introduced on the basis of bilat-eral filtering, and the curvature was taken as the third filtering influence factor to give considera-tion to noise filtering and point cloud smoothing in the filtering process. The experimental results show that the proposed multi-lateral filtering algorithm has the highest noise removal accuracy of 97.1% compared with bilateral filtering algorithm and Gaussian filtering algorithm. In the recon-struction process, details and smoothness are guaranteed, and the deviation from the real surface of the model is minimal.