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研究生:謝昌晏
研究生(外文):Chang-Yen Hsieh
論文名稱:最佳化視覺資訊與可視度完備性的模型全覽方法
論文名稱(外文):Model overview by optimizing visual information and visibilitycompleteness
指導教授:莊永裕
指導教授(外文):Yung-Yu Chuang
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:29
中文關鍵詞:模型全覽香農熵視覺資訊可視度完備性
外文關鍵詞:model overviewShannon entropyvisual informationvisibility completeness
相關次數:
  • 被引用被引用:0
  • 點閱點閱:246
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
模型的視野參數選擇是一個被廣泛研究的主題。由於整個問題包
含許多的變數,而且所謂「理想的結果」通常取決於實際上的應用,
因此不同領域的研究者關心此的問題不同部份,而非嘗試為整個問題
提供一個一般解。此問題的其中一個應用是,如何自動產生數個視點
或者一段全覽影片(overview video),來幫助使用者了解一個三維模
型。這個應用的困難之處在於如何評估一個視點或是一段全覽影片的
好壞。在這篇論文中,我們介紹一個基於香農熵(Shannon Entropy)
的評估法,用以比較不同全覽影片所含有的總資訊量。對於一個固定
光照參數的三維模型,這個評估法同時考慮了視覺上的品質以及可視
度上的完備性。此外,該評估法可以簡單的以今日的繪圖硬體來實
作,因此可以在合理的時間內完成所需的計算。於是我們可以使用此
評估法加上攝影機移動的限制式,自動的產生一段平滑的攝影機路徑
來得到三維模型的全覽影片。
Selection of viewing parameters is a widely studied problem. Researchers from different areas pay attentions to different parts of the problem because the complete one involves too many variables, and the ideal results highly depend on the concrete applications. One of the topics is to understand a
3D model by automatically generated views or overviewvideos. The difficulty comes from how the quality of views and videos be evaluated. In this thesis, we introduce a measure based on Shannon entropy to compare the amount of information for different overview videos. The measurement considers both visual quality and visibility completeness of the target 3D model with fixed lighting parameters. Furthermore, the measurement can be easily implemented on graphics hardware so the computation can be completed in reasonable time. We then combine the measurement with camera movement constrains to automatically generate a smooth camera control path that provides an overview for the model.
口試委員會審定書 i
摘要 iii
Abstract v
1 Introduction 1
1.1 Overview video . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . 2
2 RelatedWork 3
2.1 Measurement . . . . . . .. . . . . . . . . . . 3
2.2 Optimization . . . . . . . . . . . . . . . . . 4
3 Measurement 5
3.1 Visual Information . . . . . . . . . . . . . . 5
3.2 Visibility Completeness . . . . . . .. . . . . 8
3.3 GPU Implementation . . . . . . . . . . . . . . 11
4 Optimization 13
4.1 Energy function . . . . . . .. . . . . . . . . 14
4.2 Viewpoint sampling . . . . . . . . . .. . . . 15
4.3 Quantum annealing . . . . . . . . . . . . . . 15
5 Results 17
6 Conclusion and Future Work 25
Bibliography 27
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