博碩士論文 983202080 詳細資訊




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姓名 洪祥恩(Hsiang-en Hung)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 以地面及空載光達點雲重建複雜物三維模型
(Reconstruction of 3D Models for Complex Buildings from Ground-based and Airborne LIDAR Point Cloud Data)
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摘要(中) 光達是近年來一個受重視的新光學遙感探測技術。對於三維模型重建而言,雷射掃描點雲資料具相當價值,能細緻地重建建物表面,如牆面、屋頂面、柱子、窗戶等細部的物件特徵。由於點雲資料為一群離散的三維座標點群,難以直接利用原始資料中所隱含的三維資訊,因此在產生三維建物模型前,必須先經過前處理,例如:組織不規則三角網、點雲資料區塊化、結構特徵辨識、結構特徵萃取等等。
以光達點雲重建三維房屋模型可達高細緻度層級,但由於獲取資料形式之限制,欲得到精細準確的三維模型,重建過程繁複。本研究以空載及地面光達點雲為資料來源,重建符合 OGC CityGML LOD3 層級規範的三維房屋模型。過程主要分為三個部分:光達點雲資料套合、點雲群組化及三維表面重建。首先對目標測區獲取空載及地面光達點雲資料後,以七參數轉換方式對空載及地面點雲進行套合。接著根據點雲之幾何條件如共面條件等,將點雲群組化,使同群點雲具相同幾何條件。接著,以不同策略針對平面點群及曲面點群重建三維表面,最後整合重建結果產生完整三維模型,並敷貼高解析紋理影像於模型表面。重建之模型並以地面實測方式驗證模型精度。
摘要(英) LIDAR, Light Detection And Ranging, is an emerging technique for optical remote sensing recently. Laser scanned point cloud is a valuable data source for building reconstruction because it can recover detailed building façade structures like wall, roof, pillar and window etc. Since the point cloud data are discrete, it is difficult to acquire useful 3D information directly. Therefore, before the generation of building models, necessary pre-processing, such as triangulated irregular network (TIN) organization, segmentation, or feature extraction etc., must be carried out.
Modeling from point cloud data can achieve high level of detail. However, due to the limitation of data, there will be complex procedures to reconstruct a highly accurate model. This research aims to reconstruct building models conforming to OGC CityGML LOD3 standard from LIDAR point clouds. The proposed method is divided into three main parts: data registration, points partitioning and surface reconstruction. First, after acquiring point cloud data from airborne and ground-based LIDAR, they are merged to a single dataset using 7-parameter transformation. The merged point cloud data are then partitioned into several groups according to different conditions such as coplanarity etc. For each point group, a three-dimensional surface is constructed based on Least Squares Method. Finally all surfaces are merged to reconstruct 3D models, and high resolution images are used as façade texture. The accuracy of reconstructed models are evaluated with ground measurement of check points.
關鍵字(中) ★ 光達
★ 點雲資料
★ 模型重建
★ 最小二乘法
★ 細緻度層級
★ 群組化
關鍵字(英) ★ LIDAR
★ Point Cloud
★ Model Reconstruction
★ Partition
★ Least Squares Method (LSM)
★ Level of Detail (LOD)
論文目次 摘要 I
ABSTRACT II
致謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章 序論 1
1.1 研究背景 1
1-2 細緻化層級 (Level of Detail, LOD) 3
1-3 光達及光達點雲資料 4
1-4 研究動機與目的 6
1-5 論文架構 10
第二章 文獻回顧 12
2-1 以航照資料重建三維模型 12
2-2 光達點雲資料重建三維模型 13
2-3 融合資料重建三維模型 15
2-4 點雲群組化方法回顧 16
第三章 研究方法與流程 19
3-1 研究方法綜述 19
3-2 資料前處理 (Point data pre-processing) 22
3-3 點雲資料套合 (registration) 27
3-4 點雲群組化 (partitioning) 31
3-5 模型三維表面重建 (surface reconstruction) 36
3-5-1 平面點雲表面重建 (Plane points reconstruction) 40
3-5-2 曲面點群表面重建 (Curve points reconstruction) 42
第四章 實驗成果與分析 48
4-1 儀器簡介 48
4-2 控制點取得與驗證指標 52
4-2-1 自由測站法 52
4-2-2 驗證指標 55
4-3 實驗案例一 56
4-4 實驗案例二 72
第五章 結論及未來展望 89
參考文獻 92
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指導教授 蔡富安(Fuan Tsai) 審核日期 2011-8-18
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