摘要
点云去噪的效果对三维扫描过程后续的曲面拟合与造型设计至关重要,如何快速准确提取特征点已成为研究热点,然而点云去噪的关键之处在于奇异值与离群值的检测。提出耦合多特征点参数的去噪模型,分别讨论每个特征点参数对去噪模型的影响程度;采用群智能算法求解出一组最优参数权重,以此确定点云去噪模型,从而达到三维散乱点云最优去噪效果;通过对Bunny模型进行去噪仿真以及某一型号的蒙皮进行去噪实验,对去噪模型进行验证。结果表明:本文提出的点云去噪模型相较于半径滤波器、统计滤波器、改进体素滤波结合高斯滤波模型,迭代更快、耗时更少,具有更好的去噪效果。
The effect of point cloud denoising is very important to the subsequent surface fitting and modeling design in 3D scanning process.How to extract feature points quickly and accurately has become a research hotspot.However,the key point of point cloud denoising lies in the detection of singular values and outliers.Therefore,a denoising model with coupled multi-feature point parameters is proposed,and the influence of each feature point parameters on the denoising model are discussed respectively.The swarm intelligence algorithm is used to solve a set of optimal parameter weights to determine the point cloud denoising model,so as to achieve the optimal denoising effect of three-dimensional scattered point clouds.The denoising simulation of Bunny model and the denoising experiment of a certain type of skin are used to verify the denoising model.The results show that the point cloud denoising model proposed in this paper has faster iteration,less time-consuming and better denoising effect than that of radius filter,statistical filter and improved voxel filter combined with Gaussian filtering algorithm.
作者
李彬鹏
茅健
杨杰
蔡航
LI Binpeng;MAO Jian;YANG Jie;CAI Hang(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《航空工程进展》
CSCD
2023年第6期45-55,共11页
Advances in Aeronautical Science and Engineering
关键词
三维散乱点云
点云去噪
群智能算法
点云特征点
3D scattered point cloud
point cloud denoising
swarm intelligence algorithm
point cloud feature points
