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研究生:蘇奕凌
研究生(外文):Su, I-Ling
論文名稱:結合影像暨感測器資訊之三維模型重建研究
論文名稱(外文):Based on the information of image and IMU sensor for 3D reconstruction
指導教授:李忠謀李忠謀引用關係
指導教授(外文):Lee, Greg
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊工程學系
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:2017
畢業學年度:105
語文別:中文
論文頁數:80
中文關鍵詞:三維重建感測器運動恢復結構完全仿射不變特徵擷取
外文關鍵詞:3D ReconstructionSensorStructure from MotionAffine-SIFT
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三維重建(3D Reconstruction)技術是利用多張多視角影像,將二維投影恢復物體三維空間之方法,類似人類雙目定位概念;從平面影像還原成立體模型如真實物體,表現出更豐富的細節資訊,而三維模型的呈現實現於生活廣泛之應用。
本研究以行動裝置為平台,對物體進行環繞拍攝取樣,透過運動恢復結構之影像演算法,無須事先校正相機參數,即計算出相機姿態與場景幾何相對關係;此外,加上感測器的地理資訊輔助,其強健穩定的特性,二次驗證定位方法,對齊校正座標,增加三維模型之精準度、提高運算效能。
3D reconstruction is the process of capturing the shape and appearance of real objects from the keyframe of different viewpoint. Through the projection of two-dimensional materials to restore three-dimensional space, which is similar to a binocular vision for the position. Nowadays, a 3D model is implemented in many applications, from an image reverts into a stereoscopic model as the original real object, that can be given more details of texture and structure.

First, based on the mobile device to scan around the object for video recording, using structure from motion(SfM) algorithm to calculate the relationship of camera position and scene geometric. Meanwhile, at the scanning stage, the sensor data are acquired along with tracks of features in the video. All these data are used to build a camera trajectory using above image techniques after scanning is completed. According to information support of sensor geography with robustness and stability, which can be demonstrated the second validation on positioning, not only enhance the accuracy of the 3D model, but also improve the efficiency.
附圖目錄 III
附表目錄 IV

第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 研究範圍與限制 3
1.4 論文章節架構 3

第二章 文獻探討 5
2.1 現有技術概況 5
2.2 背景知識及相關原理 7
2.2.1 特徵擷取 8
2.2.2 相機場景幾何 12
2.2.3 感測器資訊 17

第三章 研究方法 22
3.1 系統架構 22
3.2 關鍵畫面截取 (KeyFrame Extraction) 25
3.3 從運動求得結構 (Structure from Motion, SfM) 27
3.3.1 三維重建 29
3.4 感測器資訊輔助 (IMU sensor Information) 30
3.4.1 定義座標系 30
3.4.2 GPS優先使用法 34
3.4.3 7-DoF相似度轉換(Similarity Transform) 37

第四章 實驗規劃 42
4.1 實驗環境 42
4.2 實驗 43
4.2.1 實驗一:以物體為對象 43
4.2.2 實驗二:以感測器資訊為輔助 53
4.2.3 實驗三:以影像張數為對象分析 56

第五章 結論 59
5.1 未來工作 59

附錄A 60
附錄B 68
參考文獻 79
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[12] Tango. URL: https://www.google.com/atap/project-tango/.
[13] P. Tanskanen, K. Kolev, L. Meier, F. Camposeco, O. Saurer, and M. Pollefeys. “Live metric 3d reconstruction on mobile phones”. In: Computer Vision (ICCV), IEEE International Conference on pp. 65–72, 2013.
[14] Image processing. URL: https://en.wikipedia.org/wiki/Image_processing.
[15] J.M. Morel and G.Yu. “ASIFT: A New Framework for Fully Affine Invariant Image Comparison”. In: SIAM Journal on Imaging Sciences, vol. 2, issue 2, 2009.
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[17] Sensor. URL: https://en.wikipedia.org/wiki/Sensor.
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[19] AGPS. URL: https://en.wikipedia.org/wiki/Assisted_GPS.
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[21] GPS Accuracy. URL: http://www.gps.gov/systems/gps/performance/accuracy/.
[22] Frank van Diggelen and Per Enge. “The World's first GPS MOOC and Worldwide Laboratory using Smartphones”. URL: https://www.ion.org/publications/abstract.cfm?articleID=13079.
[23] Manolis I. A. Lourakis, Manolis I. A. Lourakis and Antonis A. Argyros. “The design and implementation of a generic sparse bundle adjustment software package based on the levenberg-marquardt algorithm”. In: Institute of Computer Science-FORTH, Heraklion,. Technical Report 340, 2004.
[24] S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu. “ An optimal algorithm for approximate nearest neighbor searching in fixed dimensions”. In: J. ACM 45, 891–923, 1998.
[25] M. A. Fischler and R. C. Bolles. “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography”. In: Communications of the ACM, vol. 24, p. 381–395, 1981.
[26] K. Mikolajczyk and C. Schmid. “An affine invariant interest point detector”. In: The Seventh European Conference on on Computer Vision Part I, Copenhagen, 28-31 May 2002, 128-142.
[27] David R. Nilosek, Derek J. Walvoord and Carl Salvaggio. ”Assessing geoaccuracy of structure from motion point clouds from long-range image collections" In: Optical Engineering 53(11), 113112, 27 November 2014.
[28] K. Mikolajczyk and C. Schmid. “An affine invariant interest point detector”. In: The Seventh European Conference on on Computer Vision Part I, Copenhagen, 28-31 May 2002, 128-142.
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[30] J. Matas, O. Chum, M. Urban, and T. Pajdla. “Robust wide baseline stereo from maximally stable extremal regions”. In: Image and vision computing Volume 22. p.761-767, 2004.
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