本論文的研究涉及點雲萃取和自動建模兩個主要方面。在點雲萃取方面，我們面臨了高維度數據、噪音和缺失數據以及不規則性和無序性等困難。為了解決這些問題，我們利用機器學習的方法從點雲數據中提取特徵，並進行點雲的分類、分割和重建等任務。機器通過學習的方式對數據進行特徵提取，我們能夠有效地處理高維度的數據。 此外，本論文還提出了自動建模技術，該技術結合了點雲的分割、主值分析和傅立葉分析等方法。通過對點雲進行分割，我們可以將點雲數據中的不同部分區分開來，以便更好地進行後續的建模操作。同時，利用主值分析的方法，我們可以對部件整體的法向量進行提取和分析，從而獲得更準確的三維模型，並利用傅立葉分析解決主值分析無法區分的形狀。這些技術的結合使我們能夠從點雲數據中提取物體的幾何訊息並自動建模，最終生成三維模型。 ;This thesis involves point cloud extraction and automatic modeling. In point cloud extraction, we face difficulties such as high-dimensional data, noise and missing data, and irregularity and disorder. In order to solve these problems, we use the method of machine learning to extract features from point cloud, and perform tasks such as classification, and segmentation on point cloud. Machines extract features from data through learning, enabling us to effectively process high-dimensional data. In addition, this research proposes an automatic modeling technique that combines methods such as segmentation of point clouds and principal components analysis. By segmenting the point cloud, we can distinguish different parts of the point cloud for better subsequent modeling operations. Using the method of principal value analysis, we can extract and analyze the normal vector of the whole part, so as to obtain a more accurate 3D model. The combination of these technologies enables us to extract object information from point cloud data and automatically model, and finally generate a 3D model.
