一种改进的自适应阈值分块曲面滤波方法

An Improved Block-index Curved Filtering Method with Adaptive Threshold

针对传统的分块曲面滤波算法使用固定阈值进行滤波时造成的误分现象,提出了一种改进的自适应阈值分块曲面滤波算法。首先采用高斯滤波以及K-D树(K-dimensional树)滤波对异常点云进行剔除;然后利用网格法对点云进行逐级分块,并以曲面拟合的方式自动获取块域种子点,降低种子区域过大造成的滤波影响,从而建立了顾及块域面积及块域内最大高差两个因素的滤波阈值自适应模型。利用3组不同的数据对传统算法与改进算法进行滤波对比试验,结果表明改进方法不仅解决了人工选取种子点带来的问题,还能有效降低两类误差,充分验证了改进算法的可靠性。

In view of the misclassification caused by the traditional block-index curved filtering algorithm using fixed threshold,an improved block-index curved filtering method with adaptive threshold was proposed.Firstly,Gaussian filter and K-dimensionl tree filter were implemented to eliminate the abnormal point cloud.Then,to reduce the filtering effect of a extreme large seed region,the point cloud was blocked step by step with the grid method,and the seed points in the block area were automatically obtained by a way of surface fitting.An adaptive filtering threshold model considering two factors including the size and maximum height difference of the block area,was established.The filtering performance was compared between the traditional method and improved method with three different sets of data,respectively.The results show that the proposed method can not only solve the problems caused by manual selection of seed points,but also effectively reduce the two kinds of errors,which verifies the reliability of the improved algorithm.