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研究生: 徐嘉駿
Chia-Chun Hsu
論文名稱: 結合測距儀與相機之離散採樣的三維材質環境建立
Constructing Textured 3D Models from Sparse Positions by Combing Range Sensors with a Projective Camera
指導教授: 項天瑞
Tien-Ruey Hsiang
口試委員: 楊傳凱
none
鄧惟中
none
陳建中
none
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2011
畢業學年度: 99
語文別: 英文
論文頁數: 53
中文關鍵詞: 移動估計紋理三維模型三維重建仿射尺度不變特徵變換
外文關鍵詞: textured 3D models, Affine Scale-Invariant Features Transform (ASIFT
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  • 我們提出了一個新的方法來重建三維材質環境的模型,在過去的方法中,三
    維環境的重建都沒有包括材質,然而,這對於一些應用而言,是無法滿足的,例
    如:機器人導航、工業自動化等等。此篇論文針對重建三維材質環境所遭遇到的
    挑戰一一討論,最終的結果,終端用戶可以從各種不同的角度以及位置來觀看三
    維環境,並且有最小幅度的失真。我們提出的方法可以套用在各種資料收集設備
    上,像是最近很流行的kinect 感應器,或者是我們所提出的輕量型三維環境資訊
    收集設備,此設備包含了一個感光耦合元件(CCD)相機、雷射測量距儀與伺服
    馬達。


    We propose a new approach to reconstruct a textured 3D models. On past approaches
    create a 3D reconstruction models without texture. However, it is not
    enough for many applications ; robot navigation , industrial automation and so
    on. This paper addresses the challenge of reconstructing a textured 3D models,
    which end-user can see the environment from even more viewpoints without distortion.
    The new approach can apply on any kind data collection device like the
    popular kinect or proposed new lightweight 3D data acquired device by using a
    laser range finder with an servomotor and a CCD camera.

    摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Reconstruction Of Textured 3D Models . . . . . . . . . . . . . . . . 1 1.2 Motion Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1 Data Collection(3D scanning) . . . . . . . . . . . . . . . . . . . . . 11 3.2 Motion Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Point Cloud Merging . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.4 Mesh Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.5 Texture Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . 22 4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1 Textured 3D reconstruction . . . . . . . . . . . . . . . . . . . . . . 25 4.2 Motion Estimation Result . . . . . . . . . . . . . . . . . . . . . . . 28 4.3 Computation Consuming . . . . . . . . . . . . . . . . . . . . . . . 32 4.4 Kinect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . 38 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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