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系統識別號 U0026-2408201509565900
論文名稱(中文) 基於空載光達點雲之三維建物模型擷取系統
論文名稱(英文) 3D Building Model Retrieval System Using Airborne LiDAR Point Clouds
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 103
學期 2
出版年 104
研究生(中文) 陳俊元
研究生(英文) Jyun-Yuan Chen
學號 P68971030
學位類別 博士
語文別 英文
論文頁數 96頁
口試委員 召集委員-史天元
口試委員-趙鍵哲
口試委員-蔡富安
口試委員-曾義星
口試委員-饒見有
指導教授-林昭宏
中文關鍵字 空載光達  點雲分類  點雲建物重建  模型擷取 
英文關鍵字 Airborne LiDAR  Point Cloud Classification  Building reconstruction  Model Retrieval 
學科別分類
中文摘要 隨著Web 2.0應用及掃描設備的發展,大量三維建物模型被分享至網路上。本研究基於資源重複利用的觀點,提出一套新穎的空載光達建模方法,其主要概念為使用點雲建物資料作為檢索的輸入,搜尋網路上外觀相似的建物模型,以取代繁雜的點雲重建程序。基於此目的本論文主要分為兩部分,第一部份為點雲分類,另一部份為檢索系統。

在點雲分類部份,因本研究以建物點雲資料作為檢索的輸入,因此必須先獲得建物點雲資料。如何將建物點雲資料自空載光達資料中分離亦為點雲資料相關研究中重要的議題之一,常見的方法是使用點雲分類技術將建物資料自點雲中分離。點雲分類的過程中,幾何特徵扮演重要的角色,而主成份分析是目前常見獲得物局部特徵的方法之一。然而傳統主成份分析易受點雲本身的稀疏性、取樣不均勻、不完整性以及雜訊影響,導致難以獲得合理的點雲特徵。本研究針對此現象採用加權共變異數矩陣及幾何中心的計算,改善傳統主成份分析易受點雲分佈的影響。此方法對點雲資料上每個點賦予一個權重,以表示其點位在空間上的貢獻程度,透過其空間貢獻程度評估合理的共變異數矩陣以及幾何中心,獲得較佳的幾何特徵以提升點雲分類的正確度。

在檢索部分,因模型資料與點雲資料兩者為截然不同的資料,如何有效的使用點雲資料進行相似模型的檢索為主要的研究議題,而一個精准且不影響效能的形狀描述方法為主要的關鍵技術。球諧函式是一個簡單、緊湊的形狀描述子,其優點是具有少量的儲存空間及搜尋時間;在低頻球諧基底函式之中,其描述子不易受到雜訊的影響;不受點雲與模型之間姿態角與平移量的影響,任一兩個具有相似的三維形狀,皆具有相似的三維描述子;多層次的分辨率,有助於資料庫匹配檢索的時間。然而點雲資料與模型資料本質上是為完全不同的資料,因此本研究採用了資料的填補與模型再取樣的前處理程序,以解決點雲資料不完整性的影響並滿足球諧函式編碼取樣的需求。

在點雲分類的實驗中,透過光達點雲資料及模擬資料的定性與定量分析,證實使用本研究所提出的方法能夠獲得較佳的點雲分類精度。而在模型擷取系統的實驗中顯示,本研究方法對於以空載光達資料作為檢索的輸入,相較於比其他方法具有顯著的優勢。
英文摘要 With the development of Web 2.0 applications and scanning equipment, an increasing number of three-dimensional (3D) building models have been made available on web-based model-sharing platforms. Based on the concept of data reuse, a building model on the Internet is retrieved and reused for modeling instead of reconstructing a new model from point cloud through a complex and nontrivial process, namely, model-driven or data-driven modeling. A 3D building model retrieval system is proposed in this study to realize this data reuse concept. The system can retrieve similar building models from a database by using a point cloud acquired through airborne LiDAR. The proposed system consists of two main steps, namely, point cloud classification and model retrieval, aims to efficiently retrieve building models that are similar to the input point cloud in terms of shape.

First, this study focuses on building model extraction and accurate classification of LiDAR point clouds, which comprise of fundamental and critical step for the separation of different objects. In the point cloud classification, geometric features that are generally utilized in the separation of different objects play an important role in successful classification. Among the geometric features, eigen-features calculated through the principal component analysis are the commonly used geometric features; they can describe the local geometric characteristics of a point cloud. However, eigen-features calculated through the principal component analysis of a covariance matrix are sensitive to LiDAR data with inherent noise and incomplete shape sampling because of the non-robust statistical analysis. To obtain reliable eigen-features from LiDAR data and improve classification accuracy, this study introduces a method of analyzing the local geometric characteristics of a point cloud through the use of a weighted covariance matrix with a geometric median rather than the standard covariance matrix and the sample mean, which are sensitive to point distribution. In this method, each point in the neighborhood of a point is assigned a weight to represent its spatial contribution in the weighted principal component analysis and to estimate the geometric median, which can be regarded as a localized center of a shape. A LiDAR point cloud can be accurately classified with a reliable covariance matrix and geometric median, and the point clouds belonging to building models can be extracted.

Second, motivated by the concept of data reuse, an encoding approach is proposed for 3D building model retrieval through the use of LiDAR point clouds. The key to a successful model retrieval system is the accurate and efficient representation of a 3D shape. The basic idea behind the proposed method is to represent point clouds and building models with a complete set of spherical harmonics (SHs). SH is a compact and simple shape descriptor that has the advantages of reducing storage size and search time. In addition, SH representation is insensitive to noises if only the low-frequency SHs are employed. The inherent rotation-invariant property of SH encoding enables the retrieval system to address the problem of 3D rotate-transform between the point cloud and building models. The multi-resolution nature of SH encoding also allows for the efficient matching and indexing of the model database. Furthermore, a data filling and re-sampling approach is proposed to solve the problem of incomplete shapes of point clouds and the aliasing problems of SH coefficients attributed to sparse sampling of point clouds.

In the experiments of point cloud classification, qualitative and quantitative analyses on airborne LiDAR data and simulated point clouds show a clear improvement of the proposed method with improved eigen-features compared with that of standard eigen-features. The classification accuracy is improved by 1.6% to 4.5% through the use of a supervised classifier. In the experiment of model retrieval system, qualitative and quantitative analyses of LiDAR data show the clear superiority of the proposed method over other related model retrieval methods.
論文目次 摘要 ...........I
Abstract ...........III
Acknowledgements .........VII
Table of Contents.........VIII
List of Tables .......... X
List of Figures.......... XI
1 Introduction .......... 1
2 Related Work .......... 7
2.1 Point cloud classification ...... 7
2.2 3D shape descriptor and model retrieval.... 10
3 Background .........14
3.1 Introduction to airborne-based LiDAR system.... 14
3.2 Introduction to Web 2.0........ 18
4 Methodology.........21
4.1 Point Cloud Classification......22
4.1.1 Support Vector Machines... ...... 23
4.1.2 Geometric eigen-features ...... 28
4.1.3 Eigen-feature analysis of weighted covariance matrices .32
4.2 Model Retrieval System ........ 39
4.2.1 Spherical Harmonic Descriptor and Data Encoding...41
4.2.2 Data Preprocessing .......44
4.2.3 Indexing and Ranking ........ 50
5 Experimental Results and Discussion.....52
5.1 Evaluation of the Proposed Point Cloud Classification Method .54
5.1.1 Evaluation of the Proposed Weighting Scheme in wPCA...54
5.1.2 Analysis of Proposed Eigen Features.....56
5.1.3 Analysis of Point Cloud Classification .....61
5.2 Evaluation of the Proposed Model Retrieval Method...65
5.2.1 Properties of the Proposed Encoding Method ...65
5.2.2 Parameter Setting of SH Encoding ...... 68
5.2.3 Evaluation of the Proposed Retrieval Method...70
6 Conclusions and Future Work.......81
Reference...........84
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