基于统计滤波与双边滤波的点云降噪算法
Point cloud denoising algorithm based on statistical filtering and bilateral filtering
赵德鹏 1刘永生 1赵涵1
作者信息
- 1. 长安大学 工程机械学院,陕西 西安 710000
- 折叠
摘要
在获取原始三维点云数据时易受环境、设备精度等因素影响,难免会引入噪声点.为使三维点云数据能够准确表征信息,提出了一种基于统计滤波与双边滤波点云降噪算法.该算法首先使用统计滤波快速去除离群噪声点,然后使用自适应迭代估计算法对点云模型进行法矢和曲率估计,利用改进滤波因子增强算法鲁棒性,最后利用改进双边滤波完成对点云模型的平滑光顺.实验结果表明,该算法去噪效果优于其他传统滤波算法,并且能够保留点云特征信息.
Abstract
The process of obtaining the original 3D point cloud data is easily affected by environment,equipment precision and other factors,and it is inevitable to introduce noise points.In order to enable the 3D point cloud data to represent the in-formation accurately,a point cloud denoising algorithm based on statistical filtering and bilateral filtering is proposed.Firstly,outlier noise points are quickly removed by using statistical filtering.And then the normal vector and curvature of the point cloud model are estimated by using an adaptive iterative estimation algorithm.The robustness of the algorithm is enhanced by using an improved filtering factor.Finally,the point cloud model is smoothed by using an improved bilateral filtering.Experi-mental results show that the proposed algorithm is superior to other filtering algorithms in denoising effect,and can retain the feature information of the point cloud.
关键词
三维点云/统计滤波/双边滤波/法向量Key words
3D point cloud/statistical filtering/bilateral filtering/normal vector引用本文复制引用
出版年
2024
NETL
NSTL
万方数据