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研究生:黃品綱
研究生(外文):Pin-Kang Huang
論文名稱:應用於立體電視之結構光法近物深度量測重建演算法與晶片實現
論文名稱(外文):Algorithm and Chip Implementation of 3DTV Depth Map Reconstruction Based on Structure Light Scheme
指導教授:范育成范育成引用關係
口試委員:吳俊霖胡心慧李珮君
口試日期:2012-07-16
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電腦與通訊研究所
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:2012
畢業學年度:100
語文別:中文
論文頁數:128
中文關鍵詞:結構光法深度量測深度圖2D對3D轉換
外文關鍵詞:Structure LightDepth MeasurementDepth Map2D to 3D
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  • 收藏至我的研究室書目清單書目收藏:0
近年來3D相關產業蓬勃發展,3D內容的需求量相對的也越來越大。3D內容的產生主要是仰賴著DIBR(Depth Image Based Rendering)的技術來產生,其僅需要藉由一張圖像以及其相對應的深度圖輸入,經過處理後即可產生虛擬多視角影像,接著就可以藉由3D顯示器達到立體的視覺效果。因此,深度資訊的萃取對於3D產業而言是很重要的。以往對於物體的深度資訊我們僅能利用相機擷取並搭配各種深度演算法計算出其深度,但是這樣的效果不彰,直到微軟推出了Kinect,其內建深度感測元件,讓我們在物體的深度萃取上多了更好的選擇。但是Kinect在設計上,主要是拿來做體感控制,所以在深度精確度上較為粗糙,對於物體細節沒辦法有效的描繪出來。
本論文實現一結構光法近物深度量測重建系統,運用投影機投射出結構光圖形,並利用數位相機接收,經過一連串邊緣萃取演算法,其中包含了雙向Sobel水平邊緣、型態濾波器、邊緣細線與修復演算法,對欲解碼的結構光編碼圖形進行處理及配對,計算出樣本產生的變化量與梯度值。並搭配建立的梯度與實際深度距離轉換資料庫,取代以往量測所使用的高複雜度三角演算法,可以快速且精確的得到實際深度值。此外,考慮到人眼對於深度感知的非線性變化,也將梯度值進行量化,使其符合人眼立體視覺感受。我們不僅可以輸出實際深度資訊也可以輸出量化後的深度圖,並可經由DIBR技術播放到3D顯示器觀看到立體效果。


3D industry vigorous growth in recent years, it leads to the requirement of 3D contents becoming more and more important. 3D contents production rely on the DIBR(Depth Image Based Rendering) technology, it only need to input a color image and its related depth map. After proceeding, we can get a virtual multi-view images and view with auto-stereoscopic 3D display. In the past, we used multi-camera to capture images and computed depth distance value via algorithm, the result quality is poor. Until Microsoft launched the Kinect, its built-in depth sensor, let us have more choice to extract the depth information. But its depth precision is rough, can not describe the detail of object. As a result, we can know the depth information is important for 3D issues.
In this thesis, we proposed an algorithm and chip implementation of 3DTV depth map reconstruction based on structure light scheme. We use a projector to project the structure light pattern and a camera to capture the pattern. After a series of edge extraction algorithm, which contains bi-direction sobel horizontal edge detection, morphology filter, edge thinning algorithm, edge recovery algorithm, pattern matching, gradient calculation, etc. Instead of triangulation algorithm, we build the gradient database to convert gradient to physical distance, it is fast and accurate. In addition, we take human’s perception into consideration, not only output the physical distance depth map but also quantificational depth map, through DIBR technology to 3D displays to view three-dimensional effects.

摘 要 i
ABSTRACT ii
誌 謝 iv
目錄 v
圖目錄 vii
表目錄 x
第一章 介紹 1
1.1 簡介 1
1.2 發展與重要性 2
1.3 研究動機 2
1.4 論文章節組成 3
第二章 文獻探討與回顧 4
2.1 接觸式掃描 4
2.2 非接觸式被動式掃描 6
2.2.1 立體光學法(Photometric Stereo) 6
2.2.2 輪廓成型法(Shape from Contours) 8
2.2.3 雙眼視差法(Binocular Stereo Vision) 10
2.3 非接觸式主動式掃描 12
2.3.1 雷射測距法(Laser Rangefinder) 12
2.3.2 結構光法(Structured Light) 13
2.4 結論 18
第三章 研究方法 19
3.1 系統架構 19
3.2 實驗環境 20
3.3 樣本編碼圖形的建立 24
3.4 前置影像處理 24
3.5 物件切割 26
3.6 邊緣萃取演算法 27
3.6.1 雙向Sobel水平邊緣偵測 27
3.6.2 形態濾波器 29
3.6.3 邊緣細線化 32
3.6.4 邊緣修復 36
3.7 深度量化 40
3.7.1 背景與物件樣本標籤化 40
3.7.2 梯度之計算與梯度資料庫的建立 43
3.7.3 量化 46
3.8 深度圖輸出 47
3.9 結論 48
第四章 硬體架構設計與實現 49
4.1 系統架構 49
4.2 Background Pattern Labeling硬體架構 50
4.3 Object Pattern Labeling硬體架構 51
4.4 Gradient Calculation硬體架構 53
4.5 Quantization硬體架構 54
4.6 結論 55
第五章 實驗方法與結果 57
5.1 模擬方法 60
5.2 實驗結果分析與比較 65
5.2.1 演算法模擬分析 65
5.2.2 文獻方法比較 91
5.3 多視角影像合成結果 100
5.4 結論 102
第六章 晶片設計流程 103
6.1 數位電路設計流程 103
6.1.1 電路規格之確立 104
6.1.2 暫存器轉換階層之電路設計與模擬 106
6.1.3 邏輯合成(Logic Synthesis) 107
6.1.4 可測試性電路設計 110
6.1.5 Gate-Level Simulation 111
6.1.6 自動佈局與繞線 113
6.1.7 DRC/LVS 驗證 114
6.1.8 Post-Layout Simulation 115
6.2 晶片實體佈局圖與其規格 115
6.3 數位晶片量測流程 117
6.4 結論 119
第七章 總結與未來展望 120
7.1 總結 120
7.2 未來展望 121
參考文獻 122
附錄A: 127
發表論文 127


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