結合多元資料重建三維房屋模型

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姓名

李唐宇(Tang-yu Li) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

結合多元資料重建三維房屋模型
(Integrating Multi-source Data for the Generation of 3D Building Models)

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★ 結合光達資料與大比例尺向量圖重建三維建物模型	★ 空載三線式掃描儀影像立體定位之研究

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摘要(中)

數碼城市可為都市規劃、建設以及管理提供重要的決策資訊。三維建物模型的建立在數碼城市中是不可或缺的。
本研究以資訊融合的方式，結合向量圖、光達以及航照資料，重建三維建物模型。重建的重點包括平頂、山型和圓弧頂之建物。研究以向量圖獲取房屋輪廓，因此研究重點為房屋模型之重建。由於圓弧頂之房屋在影像上並無明顯特徵，因此研究用光達之三維資訊來描述圓弧屋頂。為了彌補光達點雲密度可能不足之情形，研究使用航照影像獲取房屋內部結構線。研究中，首先，對三種資料分別進行前處理的作業，接著將房屋內部之光達點雲，由不同的面方程式進行擬合，分出不同屋頂類型。研究中以面方程式描述圓弧頂建物，非圓弧頂者，山型屋將以面相交方式找出屋脊線。平頂屋部份結合光達和航照影像偵測階梯線。最後利用分割-合併-模塑方法模塑產生建物模型。
本研究測試區位於新竹科學園區。向量圖比例尺為1：1000，光達點雲密度為1.5點/m2，航照影像之空間解析力為12 cm。研究成果顯示，屋頂面分類成功率可達80%，模型重建正確率為85%。建物輪廓部分均方根誤差在X 方向為0.51 m，Y方向為0.41 m。模塑誤差為0.19 m。

摘要(英)

Cyber city provides important information for the city planning, construction, and management. Three dimensional building models are the indispensable component in the cyber city.
This investigation integrates 2D maps, LIDAR data, and aerial images for building modeling. This research handles flat, gable, and cambered roofs. Vector maps are used to locate the building boundaries. Since a cambered roof does not have significant features in the image space, we use the LIDAR point clouds to model it. Because the density of the LIDAR point clouds might not be sufficient to reconstruct the internal facets of buildings, we employ aerial images. In the first step, the data preprocessing encloses the polylines of the maps then extract the point clouds that belong to a building. After filtering the point clouds, we fit the data by different surface functions. Through the roof hypothesis by employing point clouds, the camber roofs are parameterized. For non-camber roofs, the ridges of gable roofs will be intercepted by the two inclined planes. The step-edges of flat roofs are obtained by combining point clouds and image features. Then the lines are projected to the object space by ray-tracing. Finally, we shape the models by SMS method.
The test site is in the Industrial Technology Research Institute of Hsin-chu. The vector maps are with a scale of 1:1,000. The point density of LIDAR data is 1.5(point/m2), and the spatial resolution of aerial image is 12 cm. The result indicates the successful rate is 80% in building classification while the fully reconstruction rate is 85%. The RMSE of building boundaries are 0.51 m and 0.41 m in X and Y directions, respectively. The shaping error is 0.19 m.

關鍵字(中)

★ 建物模型
★ 航照影像
★ 光達
★ 向量圖
★ 建物重建

關鍵字(英)

★ LIDAR
★ building model
★ building reconstruction
★ 2D map
★ aerial image

論文目次

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VIII
表目錄 XIII
第一章前言 1
1.1 研究動機與目的 1
1.2 文獻回顧 3
1.3 研究構想與流程 7
1.3.1 資料前處理 8
1.3.2 屋頂面分類 8
1.3.3 建物模型重建 9
第二章資料前處理 12
2.1 建立封閉多邊形 12
2.2 萃取建物點雲 13
2.3 產生數值地表模型 14
2.4 建立航照方位 14
第三章屋頂面分類 15
3.1 過濾光達點雲 16
3.1.1 點群分類 17
3.1.2 標準差濾點 18
3.1.3 三角網角度判斷 18
3.2 小區塊判斷 20
3.3 多層結構判斷 20
3.4 擬合屋頂面 22
3.4.1 屋頂區塊分割 22
3.4.2 擬合分析 23
3.4.3 斜率分析 26
3.5 凹多邊形處理 26
第四章建物模型重建 30
4.1 屋頂共面分析 31
4.1.1 區塊成長法 32
4.1.2 屋頂面修正 34
4.2 物空間特徵萃取 36
4.2.1 邊界偵測 36
4.2.2 過濾邊界 36
4.2.3 建立工作區 37
4.3 像空間直線偵測 38
4.3.1 霍氏直線偵測 38
4.3.2 錯誤線段處理 40
4.4 建立三維線段 42
4.4.1 判定候選屋頂面 42
4.4.2 光線追蹤法 44
4.5 建立屋脊線 45
4.6 建物模塑 48
第五章測試成果及討論 50
5.1 研究資料簡介 50
5.2 研究流程中各門檻值說明 53
5.3 資料前處理之成果 56
5.4 屋頂面分類之成果及討論 58
5.4.1 多層屋頂過濾成果 58
5.4.2屋頂面分類成果 60
5.4.3 平頂屋測試 61
5.4.4 山型屋測試 63
5.4.5 圓頂屋測試 65
5.4.6 弧頂屋測試 67
5.4.7 面分類錯誤例 69
5.5 建物模型重建之成果及討論 72
5.5.1建物模型重建成果 72
5.5.2 平頂屋測試(1) 74
5.5.3 平頂屋測試(2) 75
5.5.4 平頂屋測試(3) 76
5.5.5 山型屋測試 77
5.5.6 圓頂屋測試 78
5.5.7 弧頂屋測試 79
5.5.8 部分正確例(1) 80
5.5.9 部分正確例(2) 81
5.5.10 重建錯誤例(1) 82
5.5.11重建錯誤例(2) 83
5.5.12重建錯誤例(3) 84
5.5.13 建物內部結構線評估 85
5.5.14 模塑誤差 86
5.5.15誤差來源討論 88
第六章結論與建議 89
參考文獻 92

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指導教授

陳良健(Liang-Chien Chen)

審核日期

2007-7-10

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