基于三维点云无网格处理的大型复杂锻件结构特征曲线重建

Complex large forgings are foundation and key to safety of nuclear power and maximization of national defense equipments. Effective extraction of structural characteristic scale from complex large forgings plays important role in improving quality and qualified rate of large complex forgings. With the development of 3D measurement technology, reconstructing structural feature from measured 3D data of complex large forgings becomes research hotspot. Classical mesh based methods are still used in complex large forgings 3D data processing as yet. However, the efficiency, accuracy and reliability of these methods are restricted due to the inherent confusion, strong noisy, massive, localization and structural complexity characteristic of 3D data measured from complex large forgings. People are challenged to reconstruct the structural feature of complex large forgings based on meshed methods. A novel conception is proposed in this subject, and mesh free methods will be researched in depth to feature preserving de-noise and simplify the original 3D point clouds, and robustly register the 3D point clouds. This will lay a foundation for rapid, accurate, reliable reconstruction of structural feature curve of complex large forgings. Further more, multi-scale tensor feature index will be constructed and used in identification of sampled point structural attribute. Snake model will be used to construct the structural feature curve of complex large forgings based on the feature points. This project will provide new ways for structural feature curve of complex large forgings, and have important scientific significance for deepening the fundamental research of structural feature reconstruction of complex, large, poor quality 3D bodys.

大型复杂锻件是核电安全化与国防装备大型化的基础和关键，锻件结构特征尺寸的有效提取对提升锻件质量、提高锻件合格率具有重要作用。随着三维测量手段的不断完善，利用测得三维数据重建大型复杂锻件结构特征成为当前研究热点。迄今大型复杂锻件测量数据处理仍局限于传统的网格化方法，大型复杂锻件测量数据自身的散乱性、强噪声、海量性、局部化和结构复杂性特点使得网格化处理方法的效率、精度和可靠性得不到保障。人们试图通过网格化处理重建大型复杂锻件结构特征的努力遇到巨大挑战。本项目提出通过无网格处理实现三维点云保特征消噪、精简和鲁棒拼接，为大型复杂锻件结构特征精确、快速、可靠重建提供保障；通过构建三维点云多尺度张量特征指标，实现采样点特征属性的有效识别，结合Snake模型实现大型复杂锻件结构特征有效重建。本项目可为大型复杂锻件结构特征重建提供新途径，对深化低质量大型复杂三维实体结构特征重建基础研究深化有重要科学意义。

随着三维测量手段的不断完善，利用测得三维数据重建大型复杂锻件结构特征成为当前研究热点。迄今大型复杂锻件测量数据处理仍局限于传统的网格化方法，大型复杂锻件测量数据自身的散乱性、强噪声、海量性、局部化和结构复杂性特点使得网格化处理方法的效率、精度和可靠性得不到保障。本项目提出通过无网格处理实现三维点云保特征消噪、精简和鲁棒拼接，为大型复杂锻件结构特征精确、快速、可靠重建提供保障。项目取得如下研究成果：(1)提出了基于采样点微分几何信息的三维点云消噪核函数各向异性带宽控制方法，构建了消噪核函数形状、方向随曲面特性自适应调整的三维点云各向异性无网格消噪算法；(2)建立了用以描述采样点微分几何相似度的定量评价指标，提出基于信息相似度的点对识别方法，构建用于收缩点坐标估计的信息相似度加权二次误差测度，实现给定精简率下三维点云的最小信息相似度加权二次误差无网格精简；(3)建立了 鲁棒的三维点云拼接测度，分析不同尺度参数下两种测度函数的性能特点，构建测度函数寻优过程中的尺度退化原则，提出基于曲率筛选和快速高斯变换的算法效率优化方法；(4)建立了多尺度框架下的采样点特征定量刻画指标，构建采样点法向一致性测度和切向一致性测度，实现噪声干扰下弱特征的有效识别，实现噪声环境下大型复杂锻件结构特征曲线的精确、快速、可靠重建。(5) 在课题组自行设计开发的线结构激光扫描维测量平台上进行了实验研究，通过对实测的大型复杂锻件三维点云进行无网格处理重建锻件结构特征曲线，验证课题相关理论和方法。在本项目的支持下，课题组正式发表学术论文6篇，其中期刊论文4篇，会议论文2篇，录用待发论文1篇，在审论文4篇。培养了硕士生8人，其中5人已顺利毕业，3位即将毕业。本项目可为大型复杂锻件结构特征重建提供新途径，对深化低质量大型复杂三维实体结构特征重建基础研究深化有重要科学意义。

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