基于泊松算法和多尺度特征编码网络的三维料面重构及修复  

3D material surface reconstruction and repair based on Poisson algorithm and multi-scale feature coding network

作　　者：谭福容 孙绍伦 张森[1,2,3] 陈先中[1,2,3] 赵宝永[1,2,3] TAN Furong;SUN Shaolun;ZHANG Sen;CHEN Xianzhong;ZHAO Baoyong(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Shunde Innovation School,University of Science and Technology Beijing,Foshan 528399,China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京科技大学顺德创新学院,广东佛山528399 [3]北京科技大学工业过程知识自动化教育部重点实验室,北京100083

出　　处：《冶金自动化》2024年第2期94-102,共9页Metallurgical Industry Automation

基　　金：国家自然科学基金面上项目(62173032);广东省基础与应用基础研究基金(2022A1515140109)。

摘　　要：高炉冶炼在完全密闭高压的环境下进行,无法直接观测高炉内部运行状况和料面形状,难以准确判断炉况,料面数据资源利用率不高,影响操作人员对炉顶布料制度的调整。为提高数据利用率,提升点云数据的质量和精度,本文提出双边滤波器对原始高炉料面三维点云数据进行预处理。通过泊松重建算法对滤波后的点云数据进行三维重构,搭建多尺度特征编码网络,对缺失的三维点云料面进行修复。泊松表面重构能够保留料面细节特征并平滑表面,为快速判断料面类型提供重要依据。通过提取不同尺度的点云特征信息,实现了三维点云特征增强和多层级表达,实验表明所提方法点云缺失预测误差小、点云补全形状完整,为处理含有缺失料面的点云数据提供了一种快速、高效且实用的解决方案。The blast furnace smelting is carried out in a completely closed and high-pressure environment,it is impossible to observe the internal operating conditions of the blast furnace and the shape of the material surface,making it difficult to accurately judge the furnace condition,and the utilization rate of the material surface data resources is not high,which affects the operators adjustment of the distribution system of the furnace top.In order to improve the data utilization rate and improve the quality and accuracy of point cloud data,a double-sided filter was proposed to preprocess the three dimensional point cloud data of the original blast material surface in the paper.Poisson reconstruction algorithm is used to reconstruct the filtered point cloud data,build a multi-scale feature coding network,and repair the missing 3D point cloud material surface.Poisson surface reconstruction can retain the detail characteristics of the surface and smooth the surface,which provides an important basis for quickly judging the type of the surface.By extracting the point cloud feature information of different scales,3D point cloud feature enhancement and multi-level expression are realized.Experiments show that the proposed method has small error in point cloud missing prediction and complete shape of point cloud,which provides a fast,efficient and practical solution for processing point cloud data with missing material surface.

关 键 词：高炉料面 点云去噪 泊松重构 神经网络 多尺度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TF54[自动化与计算机技术—计算机科学与技术]