成功大學博碩士論文系統

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研究生：	王彥文 Wang, Yen-Wen
論文名稱：	以K維樹為基準的均值偏移演算法來處理三維重建之雲點表面 3D Point-Cloud Surface Reconstruction by Using KD-Tree-Based Mean Shift Algorithm
指導教授：	連震杰 Lien, Jenn-Jier
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	90
中文關鍵詞：	結構光三維重建、K維樹、K個最近鄰居法、均值偏移演算法
外文關鍵詞：	3D structured light reconstruction, KD-tree, K-Nearest Neighbor, Mean Shift Algorithm
相關次數：	點閱：143 下載：0
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本論文提出一套系統，主要應用在渦卷三維表面重建的處理，主要是將雜訊取出，其他論文的作法會將雜訊去除，但是在此則會將雜訊保留並還原，並保留資料的真實性。本系統共分為兩個子系統：1)三維結構光重建系統 2)以K維樹為基準的均值偏移演算法處理系統。第一個子系統利用三維結構光來重建出物體表面的三維雲點，在重建前需要先校正DLP和攝影機的參數和相對關係，使用OpenCV的演算法來校正，校正完成後可求得DLP和攝影機之間的相對關係，接著DLP投影格雷碼條紋圖到物體表面上，並解碼得到DLP和攝影機的對應點關係來重建出物體的三維表面。第二個子系統將以重建的渦卷為例，根據觀察重建的結果來切割表面，分出渦卷的上層和下層並分別處理，首先建立高斯模型並根據觀察處理的結果來選擇合適的標準差倍數，即可取出異常雜訊，將保留取出的異常雜訊，接著將雲點建立K維樹，為了可以快速的搜尋K個最近鄰近的點，並以半徑大小的範圍來偵測點周圍的分佈密度，密度低的點將視為低密度雜訊並取出，將保留取出的低密度雜訊，再使用均值偏移演算法將取出的雜訊推回到物體的表面上，最後根據三維點雲對和二維影像的對應點關係，將深度正規化並產生深度Z圖，掃描深度Z圖並填補破洞，再回原到三維點雲。

This thesis presents a system to mainly process 3D surface reconstruction of scroll. Mainly process noise, and other thesis processing method is removing noise, but our method is noise reservation and reduction that can reserve truth of the information. The system consists of two subsystems: 1) 3D structured light reconstruction system 2) 3D Point-Cloud Surface Reconstruction by Using KD-Tree-Based Mean Shift Algorithm. The first subsystem will reconstruct 3D object surface of point cloud using structured light. Before reconstruction, DLP and camera parameters need to be calibrated by using OpenCV algorithms. After Calibration, we can obtain relationship between DLP and camera. DLP projects gray code pattern onto the surface of the object and decode gray code pattern to obtain the corresponding point between DLP and camera, then it can reconstruct 3D surface of the object. The second subsystem will be case of scroll reconstruction. According to the result observation to segment surface reconstruction and process separation of the upper and lower scroll respectively. Firstly, generate gaussian model and select adaptive standard deviations based on processing result observation to extract outlier noise. It will reserve extracting outlier noise. Next, it will build a KD-tree for point cloud to be able to quickly find K nearest neighbor points, and detect distribution density around the point based on radius. Low density point will be considered as low density noise and be extracted. It will reserve extracting low density noise. Then use mean shift algorithm to push extracting noise back on surface of the object. Finally, according to corresponding point between 2D DLP image and 3D point cloud generate depth Z map based on normalization of depth, and then scan depth Z map to fill the hole. Depth Z map will be returned to 3D point cloud.

摘要................................................................................................IV
Abstract............................................................................................V
誌謝................................................................................................VII
Table of Contents...................................................................................VIII
List of Figures.....................................................................................X
List of Tables......................................................................................XIII
Chapter 1.	Introduction........................................................................1
1.1	Motivation..................................................................................1
1.2	Related Work................................................................................3
1.3	System Flowchart............................................................................6
1.4	Organization of Thesis......................................................................11
Chapter 2.	DLP Structure Light System and Calibration..........................................13
2.1	Calibration Setup...........................................................................14
2.2	Calibration Framework.......................................................................18
2.3	Initial Camera Calibration..................................................................21
2.4	Calibration and Rectification between Camera and DLP........................................27
2.5	3D Object Reconstruction....................................................................31
Chapter 3.	3D Object Reconstruction Based on Gray Code.........................................33
3.1	Gray Code Encoding Pattern Generation Based on Binary Code Encoding Pattern.................35
3.2	3D Unit Vector Computation for DLP and Camera Planes, and Rectification.....................40
3.3	Find DLP and Camera Corresponding Unit Vectors Based on Binary Decoding Maps................45
3.4	3D Object Reconstruction Based on Line-Line Intersection of Corresponding Unit Vectors......52
Chapter 4.	3D Point-Cloud Surface Reconstruction by Using KD-Tree-Based Mean Shift Algorithm...56
4.1	3D Point-Cloud Segmentation Using K-Means Clustering........................................59
4.2	Outlier Noise Extraction Based on Gaussian Model............................................60
4.3	Fast K-Nearest Neighbor Search of 3D Point Cloud Using KD-Tree..............................62
4.4	Low Density Noise Extraction Using K-Nearest Neighbor.......................................66
4.5	3D Surface Reconstruction Using Mean Shift for Extracted Noises.............................67
4.6	Hole Filling by Interpolation...............................................................69
Chapter 5.	Experimental Results................................................................73
5.1	Data Collection.............................................................................73
5.2	Reconstruction Comparison...................................................................75
5.3	Noise Process Analysis......................................................................80
5.4	Measurement Comparison......................................................................83
Chapter 6.	Conclusion and Future Work..........................................................86
Reference...........................................................................................88

                                    

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校內：2021-08-30公開
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