几何特征保持的文物点云去噪算法

三维激光扫描是一种快速获取高精度点云的新技术，但由于受物体本身的构造、粗糙程度、纹理以及测量环境等因素的影响，获取的点云数据大多存在孤立的噪声点。针对文物点云数据模型中复杂噪声难以去除的问题，提出一种几何特征保持的点云去噪算法。首先通过栅格划分删除点云中的大尺度噪声；然后定义点云中数据点的曲率因子和密度因子，并通过对其加权构造模糊C均值聚类（Fuzzy C-means clustering, FCM）的目标函数；最后采用该特征加权FCM算法删除小尺度噪声，从而实现点云的去噪处理。实验结果表明，该几何特征保持的去噪算法对文物点云数据具有良好的去噪效果，是一种有效的点云去噪算法。

A Denoising Method for Point Cloud of Cultural Relics with Geometric Feature Preservation

Three-dimensional laser scanning is a new technology for fast acquisition of high-precision point clouds. However, due to the influence of the structure, roughness, texture of the object itself and measurement environment, the acquired point clouds mostly have isolated noise points. In view of the difficulty of removing complex noise in the point cloud data model of cultural relics, a denoising method for point cloud with geometric feature preservation is proposed. Firstly, the large-scale noise is deleted by rasterizing the point cloud, then the curvature factor and density factor of the data points in the point cloud are defined, and the clustering objective function of fuzzy C-means clustering (FCM) is constructed by weighting the factors. Finally, the small-scale noise is deleted by using the feature-weighted FCM algorithm, thus the denoising of point cloud is realized. The experimental results show that the denoising method for point cloud with geometric feature preservation has good denoising effect on cultural relics point cloud data, which is an effective point cloud denoising algorithm.