Can Machine Learning be Used as a Tool to Clean 3D Point Clouds for CAD Model Construction & Meshing?

机器学习能否用作清理 3D 点云以进行 CAD 模型构建的工具

• 批准号: 2023872
• 金额: --
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2023872
• 关键词: Machine; Learning; Used; Tool; Clean

This project addresses the problem of cleaning point clouds as a pre-processing step for creating respective CAD models and meshes, ultimately for numerical engineering applications. Methods of visualising the findings will also be explored.High resolution laser scans rarely come without noise or imperfections. These scans typically come in the form of point clouds, high resolution arrangements of points which represent surfaces. When considering engineering applications, these three-dimensional point clouds are subsequently transformed into a mesh as part of the reverse-engineering process, whereby the points are connected via the surfaces of interconnected polygons. Simulations can then be ran using these meshes and analysed using methods such as finite element analysis (FEA). The goal of cleaning these scans is to remove the points which are either not relevant for the purpose or counter-productive. Any noise or imperfections in the original scan can be exacerbated when the cloud is converted in to a mesh which will have consequences when running simulations on the model. Manually cleaning these scans can be a very time-intensive process and while many software packages offer the ability to do this, an efficient automated or semi-automated process would be beneficial. In this project, a machine learning approach is adopted to pre-process point clouds by considering the subsequent steps in the reverse-engineering and numerical engineering pipeline. The focus will initially be on classification and clustering algorithms to find and identify imperfections which could affect the analysis results in later stages.It has been previously shown that boosted random forests can be used to aid a user in point-cleaning. One investigation will be to identify if more complex machine learning algorithms can further automate this process without a drastic increase in inefficiencies such as time and computer resources. Deep learning is an exciting, relatively modern area which is showing positive applications in many areas and so, ultimately, will be explored for the aforementioned implementation.An interesting part of this project will be investigating the effects of various input features for the models. Cartesian-based (or potentially otherwise) locations, respective normals, RGB colour channels and light intensities are some of the parameters which will be considered. Variations in the parameters in the models themselves will also be tested; numbers of decision trees in forests, pooling methods and numbers of layers in neural networks, variations in k in k-clustering algorithms are examples of this. Dimensionality reduction techniques such as principal component analysis will also be investigated to see if increases in performance or efficiency can be achieved.Visualisation of the results of these processes is an important part of the later stages of this project. Good visualisation of the work done in relevant areas of the scans could provide benefits to those potentially working in manually further cleaning the scans as well as engineers working directly at the meshing and numerical-engineering stages. Comparisons of the methods of visualising the various stages of this processing will be conducted.

该项目解决了清理点云的问题，作为创建相应CAD模型和网格的预处理步骤，最终用于数字工程应用。高分辨率激光扫描很少没有噪声或缺陷。这些扫描通常以点云的形式出现，点云是代表表面的点的高分辨率排列。当考虑工程应用时，这些三维点云随后被转换成网格，作为逆向工程过程的一部分，由此点通过互连多边形的表面连接。然后可以使用这些网格进行模拟，并使用有限元分析（FEA）等方法进行分析。清理这些扫描的目标是删除与目的无关或适得其反的点。当云转换为网格时，原始扫描中的任何噪声或缺陷都会加剧，这将在模型上运行模拟时产生后果。手动清理这些扫描可能是一个非常耗时的过程，虽然许多软件包提供了这样做的能力，一个有效的自动化或半自动化的过程将是有益的。在这个项目中，通过考虑逆向工程和数值工程管道中的后续步骤，采用机器学习方法来预处理点云。最初的重点将是分类和聚类算法，以发现和识别可能会影响分析结果的不完善之处，在以后的stages.It先前已被证明，提升随机森林可以用来帮助用户在点清洗。一项调查将确定更复杂的机器学习算法是否可以进一步自动化这一过程，而不会大幅增加时间和计算机资源等效率低下的情况。深度学习是一个令人兴奋的，相对现代的领域，在许多领域都显示出积极的应用，因此，最终，将探索上述实施。这个项目的一个有趣的部分将是研究模型的各种输入特征的影响。基于笛卡尔（或潜在地以其他方式）的位置、相应的法线、RGB颜色通道和光强度是将被考虑的一些参数。模型本身参数的变化也将被测试;森林中决策树的数量，神经网络中的池化方法和层数，k聚类算法中k的变化都是这样的例子。此外，我们亦会研究减少偏差的技术，例如主成分分析，以了解是否可以提高效能或效率。这些过程的结果的视觉化，是本项目后期工作的重要部分。在扫描的相关区域中完成的工作的良好可视化可以为那些可能在手动进一步清洁扫描中工作的人以及直接在网格划分和数字工程阶段工作的工程师提供好处。将对这一过程的各个阶段的可视化方法进行比较。