博碩士論文 102383603 詳細資訊




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姓名 張清(Watchama Phothong)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 利用二維多重圖像建構三維三角網格模型的生成與品質改進
(Generation and Quality Improvement for 3D Triangular Model Constructed by 2D Multiple Images)
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摘要(中) 輪廓法(Shape-from-Silhouette)已被廣泛運用於三維模型的重建,利用從多個視角拍攝的物體影像,並擷取出物體輪廓來建立三維視覺化外殼模型。輪廓法是利用多個視角的多面體互相相交來形成視覺化的模型,然而此方法會使模型產生許多不屬於物體的虛擬特徵,此外物體表面上的凹洞無法使用輪廓法正確的描述出來。物體的特徵也存在於模型上必須在後處理中被保留下來。本研究的目的是提出一個從多個視角的物體影像輪廓重建三維模型,以及模型品質改善以解決上述的問題。本研究所提出的三維模型重建注重於利用多面體的相交產生出正確的物體模型表面點,以及移除品質不佳的三角網格。物體模型品質改善是利用一個重複疊代的步驟來達到模型優化的效果,其中第一次疊代會消除虛擬特徵以及模型上的假面,剩餘的疊代會將模型表面平滑化以及將輪廓特徵保值住。本研究所延伸的另一個目的為利用上述方法重建物體模型並加上色彩紋理運用於電子商務上做為產品的展示。論文中也利用有許多實際案例來驗正此方法的可靠性。
摘要(英) The shape-from-silhouette (SFS) method has been widely used in three-dimensional (3D) shape reconstruction. It uses silhouettes of a series of two-dimensional (2D) images of an object captured from multiple viewpoints to generate a 3D model that describes the visual hull of the object. The inherent problem with this method is the virtual features that appear all over the model because of being formed by the intersection of multiple polyhedra from different views. In addition, concavities on the object may wrongly be modeled as convex shape because they are invisible on image silhouettes. On the other hand, real object features may exist which should be preserved on the model. The purpose of this study is to propose a method for the generation of 3D model from silhouettes of multiple images as well as a quality improvement method to overcome the afore-mentioned problems. The proposed 3D modelling method focuses on accurate evaluation of 3D points intersected by all polyhedra from different views and the removal of poor meshes on triangulation. The quality improvement method is essentially an iterative procedure, in which the first iteration is used to eliminate virtual features and artifacts, and the remaining iterations are applied to smooth the model, while preserving the consistency of all silhouettes. The proposed method is designed to be used for product presentations in e-commerce, so the 3D model must be covered with the color and texture of the object. Several examples are presented to illustrate the capability of the proposed method.
關鍵字(中) ★ 輪廓法
★ 三維模型重建
★ 改善三維網格品質
關鍵字(英) ★ shape-from-silhouette
★ 3D reconstruction
★ quality improvement of 3D meshes
論文目次 ABSTRACT I
ACKNOWLEDGEMENTS III
CONTENTS IV
LIST OF FIGURES VII
LIST OF TABLES XIV
CHAPTER I INTRODUCTION 1
1.1 Introduction 1
1.1.1 3D model reconstruction 3
1.1.2 Shape from silhouettes 4
1.1.3 Quality improvement of 3D model 7
1.2 Literature review 9
1.2.1 3D modelling from 2D multiple images 9
1.2.2 Shape from silhouette method 12
1.2.3 Meshes optimization method 14
1.3 Research objectives 16
1.4 Research methodology 17
1.5 Organization of the dissertation 21
CHAPTER II TRIANGULAR MODEL GENERATION BY THE SHAPE-FROM-SILHOUETTE TECHNIQUE 22
2.1 Introduction 22
2.2 Methodology 24
2.2.1 Polyhedra preparation 27
2.2.2 3D points calculation by polygon intersection method 28
2.2.3 3D points calculation by marching intersection method 40
2.2.4 Generation of triangular meshes 48
2.3 Results and discussion 52
2.4 Summary 70
CHAPTER III QUALITY IMPROVEMENT OF 3D MODEL 72
3.1 Introduction 72
3.2 Remeshing process 74
3.2.1 Remeshing process of 3D meshes with regular meshes 86
3.2.2 Remeshing process of 3D meshes with irregular meshes 86
3.3 Mesh smoothing process 89
3.4 Meshes simplification 96
3.5 Results and discussion 98
3.6 Summary 106
CHAPTER IV PARAMETRIC STUDY AND MODIFICATION OF QUALITY IMPROVEMENT METHOD 110
4.1 Introduction 110
4.2 Improvement of 2D silhouette contours 113
4.3 Improvement of the remeshing process 119
4.3.1 Vertex-density method 124
4.3.2 Silhouette-contours method 127
4.3.3 3D geometric method 132
4.4 Improvement of mesh smoothing process 136
4.5 Results and discussion 139
4.6 Summary 162
CHAPTER V CONCLUSION AND FUTURE STUDY 164
5.1 Conclusion 164
5.1.1 3D modelling 164
5.1.2 Quality improvement of the meshes 165
5.1.3 Modification of quality improvement method 165
5.2 Future study 166
REFERENCES 170
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指導教授 賴景義 廖昭仰(Lai, Jiing-Yih Liao, Chao-Yaug) 審核日期 2018-7-4
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