双边滤波法可以对海德堡视网膜断层扫描仪（HRT）扫描获得的三维点云数据进行有效的去噪处理。该算法在去噪的同时保留了图形的特征信息，缺点是多次迭代计算耗费了大量时间，所以该算法无法直接运用到实际的诊断中。邻域均值算法对位于某点一定邻域内所有点的Z坐标做均值处理，且根据距离中心点的远近取不同的权值，也能对图形进行去噪处理，只是单独使用虽耗费时间较少但效果远不及双边滤波算法。因此，本文提出采用邻域均值法作为双边滤波算法去噪的预处理。研究发现，该方法在保留图形特征的同时，并且在相同去噪效果的前提下可以显著减少计算时间，提高运行效率。

The noises, which come from the 3D Point Cloud data obtained by the Heidelberg Retina Tomograph (HRT), can be smoothed effectively by a bilateral filtering algorithm. This algorithm can retain the graph feature information while denoising, but the execution time of this algorithm increases greatly with the increase of the iteration number, so that this algorithm can not be applied to the diagnostic  practice. The mean neighborhood method can also smooth the graph through the average value processing to a point's Z coordinate of a certain neighborhood, and selecting different weights according to the distance from the target point, but the effect is not better than using the  bilateral filtering algorithm. So this paper presents the mean neighborhood method to perform preprocessing by bilateral filtering denoising. We find our method can significantly reduce the computing time.