點雲建模技術

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姓名

許晴然(Cing-Ran Syu) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

點雲建模技術
(Modeling Technology for Point Cloud)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 點雲
★ 建模

關鍵字(英)

論文目次

中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章緒論 1
1-1 研究背景與動機 1
1-2 相關論文回顧 2
第二章基礎原理 6
2-1 點雲資料 6
2-2 人工智慧 8
2-3數值分析 20
2-3-1 主成分分析法 20
2-3-2 展開式 22
第三章基於PointNet2之點雲萃取 24
3-1 數據集與其特性 24
3-2 網路架構 26
3-3 點雲萃取流程 29
3-3-1 物件分類 30
3-3-2 部件分割 34
3-4結論 35
第四章基於基本圖形之建模 37
4-1表面建模流程 37
4-2演算法流程圖 38
4-2-1 圖形分類 39
4-2-2 表面形狀分析 41
4-2-3 OBJ檔案編寫 47
4-2-4 基本圖形與建模後的量化分析 48
4-3 物件建模實例 52
4-3-1 桌子之建模 52
4-3-2 椅子之建模 59
第五章結論 64
參考文獻 66
中英文名詞對照表 70

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指導教授

余業緯孫慶成

審核日期

2023-8-16

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