计算机与数字工程2025，Vol.53Issue(4)：936-941,6.DOI:10.3969/j.issn.1672-9722.2025.04.004
基于极大似然法改进算法的点云去噪
Point Cloud Denoising Based on Improved Maximum Likelihood Method
唐渝钦 1蔡勇 1张建生1
作者信息
- 1. 西南科技大学制造科学与工程学院 绵阳 621000||制造过程测试技术教育部重点实验室 绵阳 621010
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摘要
Abstract
As a common geometric data type,point cloud contains abundant geometric space information.However,the point cloud data obtained by acquisition equipment or image reconstruction is often disturbed by noise.It wills affect the downstream tasks of point cloud processing.Combined with the optimization idea in deep learning,this paper improves the network structure of this method,links hierarchical features,freezes the feature extractor and retrains the score estimation unit to improve the performance of the network.To better protect the characteristics of the point cloud,this paper optimizes the loss function of the network and intro-duces the regularization term,so that the points are regularly distributed on the surface.The experimental results show that the chamfer distance of the point cloud after denoising is reduced by 7.6%compared with the original network,and the denoising effect is effectively improved.The denoising effect of the improved algorithm is not only superior to state-of-the-art deep-learning-based denoisers,but also superior to state-of-the-art optimization-based denoisers.关键词
深度学习/点云去噪/极大似然法/特征保护Key words
deep learning/point cloud denoising/maximum likelihood method/feature protect分类
计算机与自动化引用本文复制引用
唐渝钦,蔡勇,张建生..基于极大似然法改进算法的点云去噪[J].计算机与数字工程,2025,53(4):936-941,6.
