請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57407完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏嗣鈞 | |
| dc.contributor.author | Wei-Hao Cheng | en |
| dc.contributor.author | 鄭惟浩 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:44:52Z | - |
| dc.date.available | 2018-08-16 | |
| dc.date.copyright | 2014-08-16 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-07-28 | |
| dc.identifier.citation | [1] W. Matusik, et al., 'Image-based 3D photography using opacity hulls,' ACM Transactions on Graphics, Vol. 21, No. 3, pp. 427-437, July 2002.
[2] M. Ribo, M. Brandner, 'State of the art on vision-based structured light systems for 3D measurements,' IEEE International Workshop on Robotic and Sensors Environments, pp. 2-6, 2005. [3] H. Schafer, F. Lenzen, and C. Garbe, 'Depth and intensity based edge detection in time-of-flight images,' IEEE International Conference on 3DTV-Conference, pp. 111-118, June 2013. [4] W. Chen, H. Yue, J. Wang, and X. Wu, 'An improved edge detection algorithm for depth map inpainting,' Optics and Lasers in Engineering, Vol. 55, pp. 69-77, April 2014. [5] K. Lai, L. Bo, X. Ren, and D. Fox, 'A large-scale hierarchical multi-view rgb-d object dataset,' IEEE International Conference on Robotics and Automation, pp. 1817-1824, May 2011. [6] Kinect for Windows. [Online]. Available: http://msdn.microsoft.com/zh-tw/hh367958.aspx [7] S. Izadi, et al., 'KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera,' in Proceedings of the 24th annual ACM symposium on User Interface Software and Technology, pp. 559-568, October 2011. [8] H. Lim, et al., 'Putting Real-World Objects into Virtual World: Fast Automatic Creation of Animatable 3D Models with a Consumer Depth Camera,' IEEE International Symposium on Ubiquitous Virtual Reality, pp. 38-41, August 2012. [9] D. Xu, et al., 'Kinect-Based easy 3D object reconstruction,' Advances in Multimedia Information Processing–PCM 2012, Springer, Berlin, Heidelberg, pp. 476-483, December 2012. [10] EmguCV. [Online]. Available: http://www.emgu.com/wiki/index.php/Main_Page [11] J. Canny, 'A computational approach to edge detection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 679-698, November 1986. [12] C. Rother, V. Kolmogorov, and A. Blake, 'Grabcut: Interactive foreground extraction using iterated graph cuts,' ACM Transactions on Graphics, Vol. 23, No. 3, pp. 309-314, August 2004. [13] JJ. Hernandez-Lopez, et al., 'Detecting objects using color and depth segmentation with Kinect sensor,' Procedia Technology, pp. 196-204, 2012. [14] Z. Tomori, R. Gargalik, and I. Hrmo. 'Active Segmentation in 3D using Kinect Sensor,' in Proceedings of the 20th International Conference on Computer Graphics, Visualization, and Computer Vision, pp. 163-168, 2012. [15] G. Triantafyllidis, M. Dimitriou, T. Kounalakis, and N. Vidakis, 'Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching,' Electronic Letters on Computer Vision and Image Analysis, Vol. 12, No. 2, pp. 78-87, 2013. [16] M. Krainin, B. Curless, and D. Fox, 'Autonomous generation of complete 3d object models using next best view manipulation planning,' IEEE International Conference on Robotics and Automation, pp. 5031-5037, May 2011. [17] P. Sandilands, M. G. Choi, T. Komura, 'Capturing close interactions with objects using a magnetic motion capture system and a RGBD sensor,' Motion in Games, Springer, Berlin, Heidelberg, pp. 220-231, November 2012. [18] A. R. Price and K. G. Ricks, 'Kinect-based Object Reconstruction,' in Proceedings of ISCA International Conference on Computer Applications in Industry and Engineering, pp. 275-280, November 2012. [19] B. Curless and M. Levoy, 'A volumetric method for building complex models from range images,' in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pp.303-312, August 1996. [20] Robotics Lab. [Online]. Available: http://roboticslab.uc3m.es/roboticslab/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57407 | - |
| dc.description.abstract | 在Microsoft Kinect問世之後，深度的資訊處理已被廣泛的研究，進而產生三維重建此一熱門議題，常被應用於醫學、考古學、藝術品建模等等。本論文透過Kinect攝影機之深度與色彩資訊擷取，經由使用者手持Kinect對物件進行環繞拍攝，系統可即時建構出所拍攝物件之彩色紋理三維模型。利用物件與所在平面之深度值差與深度斜率變化來偵測物件邊緣，並將畫面中之線條配合EmguCV尋找出多個輪廓，經由深度資訊與輪廓大小之門檻，來判斷物件輪廓的所在位置，進而將物件輪廓內之深度值與色彩資訊配合KinectFusion，即可得到物件的三維紋理模型，使用者可即時的觀察場景與物件之三維重建過程。以本論文之系統與Kinect RGBDemo v0.7.0之程式做比較，本篇論文之環境設置較為簡單，且物件重建的結果也較為精緻。 | zh_TW |
| dc.description.abstract | Since the debut of the Microsoft Kinect in 2010, depth information has been extensively applied to a variety of areas in academic research and industrial products. Among areas in which depth information plays a key role, 3D reconstruction has become a popular issue, with applications in medical imaging, archeology, and art reconstruction. With a user holding a Kinect camera which captures RGB and depth streams and moving around the target object, a textured 3D model can be reconstructed in real-time. The main purpose of this research is to 3D reconstruction in real-time using a single Kinect. To this end, we first define appropriate depth thresholds to perform the edge detection. Then we use EmguCV method findcontours to check for contours. Finally, we reconstruct a textured model from data of contours with KinectFusion. Experimental results are compared against the results of an existing system, showing the accuracy and effectiveness of the proposed design. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:44:52Z (GMT). No. of bitstreams: 1 ntu-103-R01921095-1.pdf: 3570744 bytes, checksum: 1b3bb65b627f452c0e19b8444d750d39 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 VIII Chapter 1 緒論 1 Chapter 2 相關研究 4 2.1 邊緣偵測 4 2.2 物件分割 5 2.3 三維重建 7 Chapter 3 即時物件建模系統 9 3.1 深度資訊之邊緣偵測 10 3.1.1 深度雜訊處理 10 3.1.2 深度差值 11 3.1.3 深度斜率 12 3.1.4 邊緣雜訊處理 14 3.2 基於輪廓之物件分割 15 3.2.1 EmguCV 15 3.2.2 膨脹 16 3.2.3 輪廓 17 3.2.4 輪廓資訊 18 3.3 物件三維重建經由KinectFusion 19 3.3.1 深度資訊轉換 19 3.3.2 ICP演算法 20 3.3.3 三維資訊建立 21 3.3.4 物件重建 23 3.4 紋理貼圖 25 Chapter 4 實驗結果 27 4.1 Kinect RGBDemo v0.7.0實驗 27 4.1.1 RGBDemo實驗-眼鏡盒 29 4.1.2 RGBDemo實驗-書本 31 4.2 本論文方法之物件建模實驗 33 4.2.1本論文方法之物件建模實驗-眼鏡盒 36 4.2.2本論文方法之物件建模實驗-書本 40 4.3 RGBDemo與本論文實驗之比較 44 Chapter 5 結論與未來展望 47 REFERENCES 48 | |
| dc.language.iso | zh-TW | |
| dc.subject | 輪廓 | zh_TW |
| dc.subject | 紋理模型 | zh_TW |
| dc.subject | 三維重建 | zh_TW |
| dc.subject | 邊緣偵測 | zh_TW |
| dc.subject | KinectFusion | zh_TW |
| dc.subject | 3D Reconstruction | en |
| dc.subject | Edge Detection | en |
| dc.subject | Contours | en |
| dc.subject | KinectFusion | en |
| dc.subject | Textured Model | en |
| dc.title | 利用單一Kinect實現即時之物件三維重建 | zh_TW |
| dc.title | Real-time Reconstruction of 3D Objects Using a Single Kinect | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 郭斯彥,雷欽隆,黃秋煌,莊仁輝 | |
| dc.subject.keyword | 三維重建,邊緣偵測,輪廓,KinectFusion,紋理模型, | zh_TW |
| dc.subject.keyword | 3D Reconstruction,Edge Detection,Contours,KinectFusion,Textured Model, | en |
| dc.relation.page | 50 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-103-1.pdf 未授權公開取用 | 3.49 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。