针对传统简化算法对点云数据的适用性不强、耗时较长、尖锐特征点的精简效果不佳等问题，提出一种基于特征贡献值及曲面分级的点云简化算法。首先利用体素化重心下采样算法对点云数据进行粗简化处理；然后根据局部几何特征确定出点云的最佳邻域范围，再根据该邻域范围计算点云数据的局部贡献值，并提取出点云特征点；最后，使用改进的k-means算法对点云数据分类，按照类别和贡献值对点云进行精简化。对斯坦福公共点云数据集和文物点云数据集分别进行了简化实验，结果表明，与曲率采样法、随机采样法和点特征保留精简法相比，所提算法的简化准确度分别提高了约70%、50%和20%，能够在保持特征区域的同时，较好地简化点云非特征区域并有效避免孔洞，实现点云准确简化。

To address the issues of traditional simplification algorithms being unsuitable for point cloud data， time-consuming， and ineffective at simplifying sharp feature points， a point cloud simplification algorithm based on feature contribution values and surface grading was proposed. Firstly， the voxelized centroid down-sampling algorithm was used for coarse simplification of point cloud data. Then， the optimal neighborhood range of the point cloud was determined according to local geometric features， the local contribution values of the point cloud data were calculated within this neighborhood range， and the feature points of the point cloud were extracted. Finally， an improved k-means algorithm was used to classify the point cloud data， and the point cloud was simplified according to categories and contribution values. Simplified experiments were conducted on the Stanford public point cloud dataset and the cultural relic point cloud dataset， and the results showed that compared with the curvature sampling method， random sampling method， and point feature preservation simplification method， the proposed algorithm improved the simplification accuracy by about 70%， 50%， and 20%， respectively. It can effectively simplify the non feature areas of the point cloud while maintaining the feature regions and avoiding holes， achieving accurate point cloud simplification.