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研究生： 張智安
Tee-Ann Teo
論文名稱： 整合光達點雲與地形圖模塑建物之分治策略
A Divide-and-Conquer Strategy for Building Reconstruction Using Lidar Point Clouds and Topographic Maps
指導教授： 陳良健
Liang-Chien Chen
口試委員:
學位類別： 博士
Doctor
系所名稱： 工學院 - 土木工程學系
Department of Civil Engineering
畢業學年度： 96
語文別： 英文
論文頁數： 119
中文關鍵詞： 重建 、分治策略 、地形圖 、房屋 、光達
外文關鍵詞： Lidar, Topographic Maps, Building, Reconstruction, Divide-and-Conquer Strategy
相關次數： 點閱：8  下載：0 
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    • 數碼城市是真實城市於資訊系統中建構的數位式虛擬版，數碼城市可應用於城市之規劃、設計、建設、及管理等。房屋模型為數碼城市中重要的元件之一。在傳統航測製圖作業中，使用航空影像立體對重建房屋模型。近年來，光達系統技術漸趨成熟，提供了另一類資料進行房屋建模，因此，本研究之目的為使用光達點雲進行房屋重建。
    本研究的第一部份為使用分治策略結合光達點雲及地形圖重建房屋模型。主要工作包含三個步驟：(1)房屋分解，(2)房屋基元形塑，及(3)房屋基元合併。在房屋分解時，使用光達資料偵測屋頂結構線，並利用該屋頂結構線分解地形圖之房屋輪廓，以產生許多簡單的二維房屋基元。接著，使用每一個房屋基元內的光達點雲形塑平面或弧面之屋頂。最後，考量基元間之共面及共線特性將三維房屋基元合併為一房屋模型。
    本研究的第二部份為點雲密度與屋頂分割及屋頂形塑之模擬與分析。由於光達系統之掃描特性，使用其掃描點隨機的分佈在地表面，因此點雲密度是複雜建物形塑的關鍵因素。本研究探討點雲密度、雜訊比例、屋頂複雜度及形塑精度之關係。模擬分析成果顯示，增加點雲密度可提升形塑精度。且平頂及弧頂建物在雜訊比例分別小於30%及15%時，可達到15公分之精度要求。
    實驗中分別用台北及屏東地區資料進行測試，重建之成功率可達90%且漏授率低於5%，房屋模型重建之平面及高程精度優於50cm。實驗結果顯示，本研究所提出的方法可產生高可靠度之房屋模型。

    The cyber city has demonstrated its potential as a replica of the real one in urban and environmental planning, design, construction, and management. The building model is one of the most important elements in a cyber city. Traditionally, the reconstruction of building models is performed by using aerial photography. An emerging technology, the airborne lidar (Light Detection and Ranging) system provides a promising alternative. Hence, in this investigation we utilize lidar point clouds for building reconstruction.
    The first part of this investigation presents a scheme for the reconstruction of building models from lidar point clouds and topographic maps using the divide-and-conquer strategy. The proposed scheme comprises three major parts: (1) decomposition of building boundaries; (2) shaping of building primitives; and (3) combination of building primitives. In the decomposition of building boundaries, the lidar data is selected to extract the inner structure lines. Then, building boundaries are divided using the extracted feature lines by the split procedure into several building primitives. To shape the building primitives, parameter fitting is applied to shape the roof for each building primitive from lidar point clouds. The roof shapes include both planar and circular types. Finally, a least squares adjustment process which considers the co-planarity and co-linearity is used to merge the 3-D building primitives into building models.
    In the second part of this investigation the effects of point cloud density for roof splitting and roof shaping are analyzed. Since the lidar is a non-targeting sampling system, the measurements are randomly distributed over the surface. Thus, the density of point clouds is an important issue in the reconstruction of complex objects. We focus on the relationship among point density, noise level, roof complexity, and the accuracy of generated roofs. Experimental results indicate that the accuracy improves as the point density increases. In shaping accuracy results, an accuracy of 15cm may be reached when the outliers are smaller than 30%. For non-flat roofs, the same accuracy may be achieved, provided that no more than at most 15% outliers exist.
    The proposed method is tested with the data collected from Taipei and Pingdong city in Taiwan. The reconstruction rate is better than 90% while the omission error is smaller than 5%. The planimetric and vertical accuracy of the reconstructed models are both better than 50cm. The experimental results confirm that the proposed scheme produces high fidelity models.

    摘要 i ABSTRACT ii 誌謝 iv CONTENTS v LIST OF FIGURES vii LIST OF TABLES xi LIST OF ACRONYMS xii CHAPTER 1. INTRODUCTION 1 1.1 Motivation 1 1.2 Research Objectives and Scope 4 1.3 Contribution 5 1.4 Dissertation Organization 6 CHAPTER 2. LITERATURE REVIEW 7 2.1 Models for Building Representation 7 2.2 Procedure of Building Extraction 11 2.3 Degree of Automation for Building Extraction 14 2.4 Data Sources for Building Extraction 21 2.4.1 Map driven 21 2.4.2 Image driven 21 2.4.3 Elevation driven 22 2.4.4 Data fusion 23 2.4.5 Summary of Data Sources for Building Extraction 27 2.5 Roof Splitting for Building Reconstruction 29 2.5.1 Approaches to Roof Splitting 29 2.5.2 Algorithms for Roof Splitting using the Edge-driven approach 29 2.5.3 Algorithms for Roof Splitting using the Facet-driven approach 30 2.5.4 Algorithms for Roof Splitting using the Map-driven method 33 2.5.5 Summary of Roof Splitting 34 2.6 Shaping Accuracy of Lidar Data 34 2.7 Summary 38 CHAPTER 3. BUILDING RECONSTRUCTION 39 3.1 Data Preprocessing 39 3.2 Decomposition of Building Boundaries 41 3.2.1 Extraction of the Step Edge 42 3.2.2 Extraction of Ridge Lines 43 3.2.3 Geometric Regularization 47 3.3 Shaping of Building Primitives 48 3.3.1 Data Snooping 49 3.3.2 Shaping of Planar Roofs 51 3.3.3 Shaping of Curvilinear Roofs 52 3.4 Combination of Building Primitives. 59 3.5 Summary 62 CHAPTER 4. SIMULATION AND ANALYSIS 63 4.1 Analysis of Roof Splitting 63 4.1.1 Parameter Selection 65 4.1.2 Point Simulation 66 4.1.3 Roof Splitting 66 4.1.4 Reliability of Splitting 67 4.1.5 Experimental Results from Simulated Data 67 4.2 Analysis of Shaping Accuracy 71 4.2.1 Parameter Selection 73 4.2.2 Point Simulation 74 4.2.3 Shape Determination 74 4.2.4 Accuracy Assessment 76 4.2.5 Experimental Results from Simulated Data 76 4.2.6 Experimental Results from Real Data 85 4.3 Summary 88 CHAPTER 5. EXPERIMENTS AND RESULTS 89 5.1 Case I 91 5.2 Case II 98 5.3 Summary 107 CHAPTER 6. CONCLUSIONS AND FUTURE WORK 108 6.1 Conclusions 108 6.2 Limitations 109 6.3 Future Work 110 BIBLIOGRAPHY 112 CURRICULUM VITAE 119

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