臺灣博碩士論文加值系統

近年來三維建模技術快速發展，成果應用也日趨多元，如虛擬實境和擴增實境等，而獲取三維資訊的方式如被動式感測的傳統攝影測量、電腦視覺中多視角影像建模以及主動式感測光達LiDAR等技術，惟各方法有其優劣，被動式感測在特徵點不足地區建模困難，高精度的主動式感測則儀器價格昂貴。本研究以Occipital Structure Sensor深度感測器為測試儀器，係主動式發射紅外線測距並可附掛於行動裝置的深度感測器，產品可搭配相應的開發軟體，兼具便利性和經濟性，惟應用前須了解Sructure Sensor所建構的三維模型精度，以了解其可運用之領域、限制和面臨的問題。
本研究以計算體積值和特徵點距離作為三維精度評估方法，運用Skannect軟體獲取三維點雲，由點雲完成模型重建後進行體積和特徵點距離計算，以最大似然一致性演算法(MLESAC)，萃取出具有幾何型態意義(方體、球體和圓柱體)的模型並計算模型體積，以實體量測之體積和特徵點距離作為精度評估的參考，實驗結果顯示Structure Sensor建構之三維模型平均體積誤差率為1.69%至5.3%而特徵點距離誤差率1.09%至2.64%，此方法具有方便且快速獲取三維資訊，亦提供未來建構無特徵性物件模型的另一項選擇。
三維模型的應用方面，本研究透過Google ARCore和遊戲開發引擎Unity，建立測量儀器水準儀操作教學之擴增實境應用，透過本研究流程建置水準儀模型，設計水準儀各部位的點選及滑動功能，提供模擬操作體驗，增進三維建模的應用。

In recent years, 3D modeling technology has developed rapidly, and the application has become increasingly diverse, such as virtual reality and augment reality. While the acquisition of 3d information, such as the passive sensing of photogrammetry and multi-angle image modeling based on computer vision, and the active sensing of LiDAR etc., have their own advantages and disadvantages, like the modeling of passive sensing is difficult in the area with insufficient feature points, and the high-precision active sensing instrument is expensive. This study uses Occipital Structure Sensor as the test instrument, which is an active infrared ray range finder and can be attached to the mobile device. The product has corresponding development software, which is both convenient and economical. However, it is necessary to understand the 3D model precision constructed by Structure Sensor, in order to understand the applicable fields, limitations and problems.
In this study, the volume value and the distance of feature points were calculated as the 3D accuracy evaluation method, and a 3D point cloud was obtained by using Skannect software. After reconstruction of the point cloud model, the volume and the distance of feature points were calculated using The Maximum Likelihood Estimation Sample Consensus (MLESAC) to extract the model with geometric significance (square body, sphere and cylinder) . The experiment shows that the 3D model volume error of the Structure Sensor construction is in the range of 1.69% ~ 5.30%, and the feature point distance error is 1.09% ~ 2.64%.This method is convenient to obtain 3D information, and also provides another 3D modeling choice for texture-less object in the future.
In terms of the application of the 3D model, this study established the augmented reality application of teaching level’s operational process through Google ARCore and game development engine Unity. Through this research process, the level model was built and the point-select sliding function of each part of the level was designed to provide simulation operation experience and improve the application of 3D modeling.

圖目錄 II
表目錄 V
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 4
第三節 研究方法 4
第四節 研究架構與流程 5
第二章 文獻回顧 7
第一節 三維資訊獲取與三維建模 7
第二節 擴增實境技術應用 19
第三章 理論基礎 23
第一節 Structured Light深度感測技術 23
第二節 MLESAC演算法 24
第三節 擴增實境技術原理 27
第四章 實驗成果及分析 45
第一節 三維模型重建成果及精度評估 45
第二節 擴增實境實作及成果 73
第五章 結論與建議 85
第一節 結論 85
第二節 建議 87
參考文獻 89
附錄 96

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