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作者姓名(中文):鄭元棓
作者姓名(英文):Yuan-Bang Cheng
論文名稱(中文):自動產生Google街景導覽影片並提供物件偵測、影像修補與3D虛擬實境顯示
論文名稱(外文):Automatic Generation of Video Navigation from Google Street View Database with Object Detection, Image Inpainting and Stereoscopic Virtual Reality Display
指導教授姓名(中文):楊傳凱
張登文
指導教授姓名(英文):Chuan-Kai Yang
Teng-Wen Chang
口試委員姓名(中文):王照明
孫沛立
花凱龍
楊傳凱
張登文
口試委員姓名(英文):Chao-Ming Wang
Pei-Li Sun
Kai-Lung Hua
Chuan-Kai Yang
Teng-Wen Chang
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:資訊管理系
學號:d10109102
出版年(民國):108
畢業學年度:107
學期:1
語文別:英文
論文頁數:167
中文關鍵詞:Google 街景影像物件偵測影像修補HOG and Exemplar-SVMsHaar and AdaboostGPUCaffe and Faster R-CNN深度圖預測(檢測)基於深度影像的渲染三維虛擬實境360度顯示(3DVR360)Unity and HTC Vive
外文關鍵詞:Google Street ViewObject DetectionImage InpaintingHOG and Exemplar-SVMsHaar and AdaboostGPUCaffe and Faster R-CNNDepth Map PredictionDIBRStereoscopic Virtual Reality 360 (3DVR360)Unity and HTC Vive
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近十年間,在電腦科學領域已有許多關於人工智慧與深度學習的研究。同時,Google街景影像服務是我們時常會使用到的,我們能夠透過Google街景影像服務去查詢到我們想要到達目的地的街景圖。然而,卻只有很少的研究是在從事於能自動化地將Google街景影像直接轉變成一個導覽影片並且還能包括一些物件偵測與影像修補的功能;再加上,也只有很少的研究能夠將這個導覽影片轉變成一個三維虛擬實境360度的顯示,能夠讓使用者配載HTC Vive去觀看這個效果。
在我的研究裡,我嘗試結合目前最受歡迎的二項電腦科學領域的研究-深度學習(人工智慧)與虛擬實境。對於導覽影片的產生,總共我已經開發了我的系統有三個版本。第一,是稱作GSVPlayer-HH&I(Google街景播放器,具有HOG+Haar物件偵測與影像修補),我主要使用基於CPU的方法去做物件偵測與影像修補。第二,是稱作GSVPlayer-FRRCNN&I(Google街景播放器,具有Faster R-CNN物件偵測與影像修補),這版本是基於在GSVPlayer-HH&I的基礎,反而我是使用基於GPU的方法(Faster R-CNN)去做物件偵測。第三,是稱作GSVPlayer-3DVR360(Google街景播放器,具有三維虛擬實境360度的顯示)。在這版本中,我實作一系列的影像處理、單視圖的深度圖檢測、基於深度影像的渲染、與三維虛擬實境360度顯示。對於這版本,結果顯示:即使這系統有較長的運算時間的需求,但是所有的使用者仍然是對於GSVPlayer-3DVR360感到滿意。
在我的論文中,針對系統的三個版本的結果與評估,在數量上與品質上我各別地呈現出相關內容;針對三者的討論與限制,我也做出詳盡的解釋。簡單地說,本論文的總結是,我所提出的這個系統是一個完整的整合式架構,我使用正確的系統流程與方法。
針對未來的研究,有許多的潛在方向可以去探索與研究。包括使用多台運算伺服器、具有時間順序考量的單視圖的深度圖檢測之卷積式神經網路、合成許多新的影格、YOLO的物件偵測方法、與針對高解析度影像的物件偵測與影像修補。
In recent years, there are abundant researches in artificial intelligence and deep learning. At the same time, Google Street View images are often used by us. We can use Google Street View to look up the scene views of destination where we want to go to. However, there is not much work that can automatically transform Google Street View images directly to a navigation video with the functionalities of object detection and image inpainting, and there is also not much work that can make the generated navigation video used together with a HTC Vive for displaying the 3DVR360 effect.
In my works, this study tries to combine currently the two most popular computer science researches of deep learning (or artificial intelligence) and virtual reality. Totally, this study has developed the three versions of my system for the navigation video generation. First, in this GSVPlayer-HH&I (i.e. Google Street View Player with HOG+Haar and Inpainting), the system mainly adopts the CPU-based methods for object detection and image inpainting. Second, in this GSVPlayer-FRRCNN&I (i.e. Google Street View Player with Faster R-CNN and Inpainting), based on the foundation of GSVPlayer-HH&I, the system instead uses the GPU-based methods (Faster R-CNN) for object detection. Third, in this GSVPlayer-3DVR360 (i.e. Google Street View Player with Stereoscopic Virtual Reality 360 Display), the system implements a series of image processing, monocular depth map estimation, DIBR and 3DVR360 display. One of the results gained is that, even though there is a problem of longer computation time in this system, all users are still satisfied with this GSVPlayer-3DVR360.
In my dissertation, for the three versions of my system, the results and evaluations regarding both quantities and qualities are presented respectively, and the discussion and limitation are explicitly explained. In conclusion, briefly speaking, the system I proposed is a complete integrated framework.
In future works, there are several potential directions can be explored and researched, including the use of multiple computing servers, a new CNN of monocular depth estimation with the temporal sequence, synthesizing novel frames, the YOLO object detection method, and object detection and image inpainting on high-resolution images.
摘要 I
ABSTRACT III
誌謝 V
TABLE OF CONTENT VI
LIST OF FIGURES IX
LIST OF TABLES XIV
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Purposes 2
1.3 Contribution 3
1.4 Scope 3
1.5 Organization 4
Chapter 2 Related Works 6
2.1 Applications of Google Earth and Google Street View 6
2.2 Object Detection 7
2.2.1. CPU-Based Machine Learning 7
2.2.2. GPU-Based Deep Learning 10
2.3 Foreground Extraction 12
2.4 Image Inpainting 12
2.5 Depth Map Prediction 14
2.6 Depth-Image-Based Rendering 18
2.7 Stereoscopic VR360 19
Chapter 3 Google Street View Player with HOG+Haar and Inpainting 22
3.1 System Architecture 22
3.2 System Flow 25
3.3 Implementation 26
3.3.1. Preprocessing and First-Staged Inpainting 26
3.3.2. Transformation Matrices between Two Consecutive Images 30
3.3.3. Object Detection using HOG+Haar and Segmentation 33
3.3.4. Road Structure Propagation and Second-Staged Inpainting 37
3.3.5. Generation of the Inpainted Continuous Navigation Animation 39
Chapter 4 Google Street View Player with Faster R-CNN and Inpainting 43
4.1 System Architecture 43
4.2 System Flow 48
4.3 Implementation 48
4.3.1. Preprocessing and First-Staged Inpainting 49
4.3.2. Transformation Matrices between Two Consecutive Images 49
4.3.3. Object Detection using Faster R-CNN and Segmentation 49
4.3.4. Road Structure Propagation and Second-Staged Inpainting 50
4.3.5. Generation of the Inpainted Continuous Navigation Animation 51
Chapter 5 Google Street View Player with Stereoscopic Virtual Reality 360 Display 52
5.1 System Architecture 52
5.2 System Flow 57
5.3 Implementation 59
5.3.1. Image Fetching and Downloading 59
5.3.2. Image Stitching 61
5.3.3. Monocular Depth Map Estimation 66
5.3.4. Depth-Image-Based Rendering 74
5.3.5. Compressing and Uploading 84
5.3.6. Unity and 3D VR 360 Display 84
Chapter 6 Results and Evaluations 93
6.1 GSVPlayer-HH&I 93
6.1.1. System Setup 93
6.1.2. Result and Evaluation 96
6.1.3. Discussion and Limitation 113
6.2 GSVPlayer-FRRCNN&I 116
6.2.1. System Setup 116
6.2.2. Result and Evaluation 116
6.2.3. Discussion and Limitation 124
6.3 GSVPlayer-3DVR360 127
6.3.1. System Setup 127
6.3.2. Result and Evaluation 127
6.3.3. Discussion and Limitation 145
Chapter 7 Conclusion and Future Works 148
7.1 Conclusion 148
7.2 Future Works 149
7.2.1. The Use of Multiple Computing Servers 149
7.2.2. A New CNN of Monocular Depth Estimation with the Temporal Sequence 150
7.2.3. Synthesizing Novel Frames 150
7.2.4. The YOLO Object Detection Method 150
7.2.5. Object Detection and Image Inpainting on High-Resolution Images 151
7.2.6. Objective Method to Evaluate the “Smoother” Issue 151
7.2.7. More Improvements of System and Function 152
References 155
Appendix I 162
Appendix II 165
Appendix III 167
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全文檔公開日期:2024/01/30 (本校及校內區域網路)
全文檔公開日期:不公開 (校外網際網路)
全文檔公開日期:不公開 (國家圖書館:臺灣博碩士論文系統)
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