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系統識別號 U0026-0809201606442500
論文名稱(中文) 影像式空載光達點雲編碼應用於三維建築模型擷取
論文名稱(英文) Image-based Airborne LiDAR Point Cloud Encoding for 3D Building Model Retrieval
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 104
學期 2
出版年 105
研究生(中文) 陳俋臣
研究生(英文) Yi-Chen Chen
學號 P66034125
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 指導教授-林昭宏
口試委員-蔡富安
口試委員-張智安
中文關鍵字 點雲編碼  三維模型擷取  空間直方圖統計  數位城市建模 
英文關鍵字 point cloud encoding  3D model retrieval  spatial histogram  cyber city modeling 
學科別分類
中文摘要 隨著網際網路2.0技術與數位城市模型建置的發展,愈來愈多三維數位模型的創造者,在網路形式的資料共享平台上分享作品,這些三維數位模型可以應用在許多領域,例如:導航、都市規劃與虛擬實境。以往重建三維建築模型使用的是攝影測量或點雲資料,但為使資源再利用,本文提出一個三維數位模型的擷取系統。首先從網際網路收集約100萬個三維數位模型,接著用空載光達系統獲取的三維建築模型點雲,作為查詢的輸入資料,以擷取資料庫中與輸入資料的幾何形狀相似的三維數位模型,再利用擷取出的三維建築模型,有效率地建造數位城市模型。為了有效地擷取三維數位模型,常見的做法是使用一個幾何形狀描述子,來編碼資料庫中的三維數位模型,這樣的做法適用於使用三維數位模型作為查詢資料。然而我們提出的系統,是利用空載光達點雲作為查詢資料。空載光達點雲具有稀疏、雜訊以及取樣不完整的特性,相關研究的方法無法直接適用於我們的系統,因此我們提出以深度影像和空間直方圖統計為基礎的編碼方式:利用深度影像描述建築物屋頂面的表面起伏,配合幾何特徵的萃取,將幾何特徵透過空間直方圖統計得到的係數,作為編碼結果的表達方式。本研究的編碼方式,可以降低由空載光達點雲資料雜訊衍生的問題,和側面與底面不完整取樣,所帶來的編碼困難度。藉由三維建築模型的細節層次(LoD)測試,以及三維數位模型資料庫的資料擷取測試,可證本文提出的方法優於其他相關的方法,並說明由空載光達點雲資料的編碼擷取三維建築模型,以建構數位城市模型的可行性。
英文摘要 With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been made available on web-based model-sharing platforms with many applications, such as navigation, urban planning, and virtual reality. A 3D model retrieval system based on the concept of data reuse is proposed to retrieve building models similar to a user-specified query. The basic idea is to reuse existing 3D building models instead of reconstructing models from point clouds. Models in databases are generally encoded compactly with a shape descriptor to retrieve models efficiently. However, most of the geometric descriptors in related work are applicable to polygonal models only. In this study, the input query of the model retrieval system is a point cloud acquired by light detection and ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than using 3D models. Given that the roof of a building is more informative than the other parts in an airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is to encode the models in the database and input point clouds consistently. First, top-view depth images of buildings are generated to represent the geometric surface of a building roof. Second, geometric features are extracted from the depth images according to the height, edge, and plane of the building. Finally, descriptors are extracted by establishing spatial histograms and are used in the 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. Experiments on a database that includes about 1,000,000 3D models collected from the Internet are conducted to evaluate data retrieval. Results show that the proposed method is superior to related methods.
論文目次 摘要 I
Abstract III
致謝 V
List of Tables IX
List of Figures IX
Chapter 1 Introduction 1
Chapter 2 Related Work 6
2.1 Model-based Model Retrieval 6
2.2 View-based Model Retrieval 7
Chapter 3 Methodology 11
3.1 System Overview 11
3.2 Data Preprocessing 12
3.2.1 Building Roof Description 13
3.2.2 Origin Determination 17
3.3 Geometric Features 19
3.3.1 Height Feature 19
3.3.2 Line Feature 20
3.3.3 Eigen Feature 21
3.4 Spatial Histogram 24
3.5 Scale-variant Encoding 27
3.6 Encoding and Indexing 29
Chapter 4 Web-based Model Retrieval System 31
Chapter 5 Experimental Results and Discussion 34
5.1 Test Data and Computational Performance 34
5.2 Encoding Properties 35
5.3 Model Evaluation 38
5.3.1 Encoding of Various LoD models 39
5.3.2 Data Retrieval from a Database 42
Chapter 6 Conclusions and Future Work 49
References 51
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