基於多台攝影機即時三維建模

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張位群(Wei-Chun Chang) 查詢紙本館藏

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資訊工程學系

論文名稱

基於多台攝影機即時三維建模
(Real-time 3D Rendering Based on Multiple Cameras and Point Cloud)

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摘要(中)

2000年以前就有三維模型的技術，直到2009 年「阿凡達」3D電影才開始受到關注，電影裡的場景、角色、各種外星生物栩栩如生，觀賞電影者深刻的了解虛擬世界的樣貌，遊戲中的世界及特效帶給玩家全新的體驗，「魔獸世界」、「暗黑破壞神」更是3D電腦遊戲的代表作品，「數位典藏」將歷史文物數位化成容易保存的資料，三維模型才可以完整的呈現歷史文物的真實樣貌，如今各個領域皆需要非常大量的三維模型，以往的三維建模非常的耗時且需要大量的人力來完成，如果可以藉由簡單的設備來輔助，在各個領域都會有很大的幫助。
經由上述介紹可以了解建立3D模型的困難處，有鑑於此，即時3D建模成為重要的議題。Kinect為Mircosoft所研發的劃時代攝影機，在各個研究領域中都時常被用到，例如：電腦視覺及圖學…等領域。由於Kinect可以偵測每個像素距離Kinect的距離，所以Kinect能夠即時擷取的人體三維資料，並經由人體偵測演算法以產生對應的點集合，另一方面由RGB鏡頭擷取角色的真實色彩，之後便可在電腦中加以檢視。本研究便是用多台Kinect作為輸入設備，建立出立體三維之點集合，以便參考。

摘要(英)

3D model construction technic is very popular in recent decades. The movie “Avatar” is a milestone of this research; the surrounding, characters and alien creatures in Avatar are so lifelike that viewers would be impressed and shocked by the impact of virtual reality. The video game World of Warcraft and Diablo are another successful instance about 3D model. Kinect is a 3D sensor produced by Microsoft, and is widely used in many research fields, such as Computer Vision, Computer Graphics, etc. With Kinect, the task of efficiently acquiring 3D data would be possible. In other words, the distances between all the positions in Kinect vision and Kinect can be captured and reliable. According to these data, Kinect is able to divide the image into foreground (human) and background quickly and easily draw the geography of human. So this paper would integrate the traditional model building technic and much research about Kinect, and then propose a real-time 3D point cloud displaying method by multiple Kinect.

關鍵字(中)

★ 三維建模
★ 虛擬實境
★ Kinect
★ 電腦視覺
★ 電腦圖學

關鍵字(英)

★ 3D model
★ virtual reality
★ Kinect
★ Computer Vision
★ Computer Graphics

論文目次

摘要 i
Abstract ii
Acknowledgements iii
Contents iv
List of Figures vi
List of Tables viii
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Background 2
1.3 Thesis Organization 5
Chapter 2. Related Works 7
2.1 3D reconstruction 7
1. Volume-based approaches 7
2. Surface-based approaches 8
3. Depth map based approaches 9
2.2 Motion sensing camera 10
2.3 Human Detection 11
1. Pre-processing 12
2. Finding candidate positions 13
3. Scan by 3D head template 14
4. Get contour of human 15
2.4 Geometry registration 17
1. Closet point 19
2. Normal shooting 19
3. Projection 20
4. Search based on other compatibility metric 21
5. Restriction based on other compatibility metrics 21
2.5 Volumetric integration 23
Chapter 3. Proposed method 27
3.1 Calibration of Kinect vision 28
3.2 Erosion and Dilation 29
3.3 Calibration of Kinects 31
3.4 Shift of point cloud 34
3.5 Integration of point clouds 36
Chapter 4. System Architecture 40
4.1 Practical detail 41
4.2 Functions and modes 45
Chapter 5. Experiment result and Analysis 50
5.1 Calibration 51
5.2 Memory allocation and efficiency 52
Chapter 6. Conclusion and Future works 59
6.1 Conclusion 59
6.2 Future works 60
References 62

參考文獻

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指導教授

施國琛(Timothy K. Shih)

審核日期

2014-7-10

推文