Surface reconstruction technology based on cloud data has broad prospects in the fields of reverse engineering, cultural heritage protection, and smart city construction. This article studies the surface reconstruction pattern recognition technology based on scattered point cloud data. The candidate feature points are extracted according to the surface variation, and the precise method of point cloud is used to fit the clustering plane, and the feature points are selected from the candidate feature points. Use the area increase method to construct the initial grid of the specific three-dimensional point group data. In the construction process, the normal vector of the point group data does not need to be separated, but defines the angle of the normal vector of the adjacent triangular grids, thereby separating relatively flat areas. Using the projection parameterization method, the scattering points in the domain are projected onto the curved surface, and the parameter values of the projection points are counted as the parameter values of the scattering points. All sampling points on the common boundary have tangent vectors along the two directions of the boundary. The direction of the bisector of the angle between the two tangent vectors is calculated as the direction of the connection vector outside the boundary of the sampling point. It can be seen from the experimental data that the search radius of the normal vector and feature descriptor when calculating the feature description operator is 0.01 and 0.02 m, instead of 0.005 and 0.006 m of the bunny data. Using the local feature size to refine the point cloud data can reduce the number of point clouds, remove redundant data in the point cloud, and realize dynamic adjustment and adaptive reconstruction of nonuniform point clouds.

中文翻译：

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基于散点云数据的曲面重建图案识别技术

基于云数据的表面重建技术在逆向工程、文化遗产保护、智慧城市建设等领域具有广阔的前景。本文研究了基于散点云数据的表面重建模式识别技术。根据表面变化提取候选特征点，利用点云的精确方法拟合聚类平面，从候选特征点中选取特征点。使用面积增加法构造特定三维点群数据的初始网格。在构建过程中，点组数据的法向量不需要分离，而是定义了相邻三角形网格的法向量的角度，从而将相对平坦的区域分离出来。利用投影参数化方法，将域内的散射点投影到曲面上，将投影点的参数值计为散射点的参数值。公共边界上的所有采样点都有沿边界两个方向的切向量。计算两个切向量之间夹角的平分线方向作为采样点边界外的连接向量的方向。从实验数据可以看出，计算特征描述算子时法向量和特征描述子的搜索半径为0.01和0.02 m，而不是兔子数据的0.005和0.006 m。使用局部特征尺寸细化点云数据可以减少点云的数量，