考虑法向离群的自适应双边滤波点云平滑及IMLS评价方法

Adaptive bilateral filtering point cloud smoothing and IMLS evaluation method considering normal outliers

针对目前在点云双边滤波平滑算法中,人工输入不合理参数导致的点云平滑效果不佳,且易导致体积收缩及现有去噪后点云质量评价方法存在表达局限性等问题,提出一种自适应参数的点云双边滤波算法和基于隐性移动最小二乘(IMLS)的质量评价方法。首先构建KD-tree数据结构用于点云拓扑,之后搜索各点邻域,利用奇异值分解法计算法向量信息,并在双边滤波公式中引入法向离群因子以剔除邻域内离群点,然后通过扩展高斯核函数的权值计算式,在点云邻域内自适应获取空间与法向特征参数,最后应用改进模型进行点云平滑并引入IMLS方法评价点云质量。实验结果表明,考虑法向离群的自适应双边滤波点云平滑算法具有良好的去噪效果,相比其他算法体积收缩更小,且IMLS评价方法客观有效。

In order to address the problems that the unreasonable parameters of bilateral filtering lead to poor smoothing effect of point cloud, volume shrinkage, and the limitation of existing quality evaluation methods, a bilateral filtering algorithm with adaptive parameters and a quality evaluation method based on implicit moving least squares(IMLS) were proposed. Firstly, the KD-tree data structure was constructed for point cloud topology, then the neighborhood of each point was searched to calculate the normal of each point using the SVD decomposition method,and the normal outlier factor was introduced into bilateral filtering to remove outliers in the neighborhood.Additionally, the space and normal characteristic parameters were calculated according to the Gaussian kernel function extended by the neighborhood norm. Finally, the constructed bilateral filtering model was applied to the smoothing of the point cloud, and the implicit moving least squares method was introduced to evaluate the quality of smoothing. The experimental results of the point cloud with noise show that the adaptive bilateral filtering point cloud smoothing algorithm considering normal outliers could attain a good effect and result in smaller volume shrinkage compared with other algorithms, and that the IMLS evaluation method could be objective and effective.