基于均值漂移聚类的点模型简化方法

为了有效简化稠密采样点模型，提出了一个基于均值漂移（mean shift）聚类的点模型简化方法。通过mean shift迭代过程，计算点模型中点对应的局部模式点，即模态点。利用模态点代替聚集在其周围的数据点，实现对模型的简化。实验结果表明该算法能有效减少稠密采样点模型的点数，且简化速度较快，简化模型能很好地保持原始模型的几何形状。

A point model simplified method based on mean-shift clustering

To efficiently simplify the densely sampled point model, a point sample data reduction method was proposed based on the mean-shift clustering algorithm. Local mode centroids were calculated by mean-shift iterative process. These mode centroids substituting for ambient data points were used to simplify the model. Experiment results show that the algorithm can effectively simplify the densely sampled point model, the reduction speed is fast, and the simplified model can preserve the original geometric shape.