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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊永裕(Yung-Yu Chuang) | |
| dc.contributor.author | Pei-Cheng Liao | en |
| dc.contributor.author | 廖培成 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:28:36Z | - |
| dc.date.copyright | 2015-08-17 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-17 | |
| dc.identifier.citation | [1] A. Sankar and S. Seitz, 'Capturing indoor scenes with smartphones,' in Proceedings of the 25th annual ACM symposium on User interface software and technology, 2012, pp. 403-412.
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Sagues, 'Localization and matching using the planar trifocal tensor with bearing-only data,' Robotics, IEEE Transactions on, vol. 24, pp. 494-501, 2008. [33] M. Aranda, G. López-Nicolás, and C. Sagüés, 'Omnidirectional visual homing using the 1D trifocal tensor,' in Robotics and Automation (ICRA), 2010 IEEE International Conference on, 2010, pp. 2444-2450. [34] MagicPlan. Available: www.sensopia.com/ [35] R. G. von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, 'LSD: A fast line segment detector with a false detection control,' IEEE Transactions on Pattern Analysis & Machine Intelligence, pp. 722-732, 2008. [36] Matlab function lsqnonlin. Available: http://www.mathworks.com/help/optim/ug/lsqnonlin.html [37] V.360° HD Camera. Available: http://www.vsnmobil.com/products/v360 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19943 | - |
| dc.description.abstract | 我們發展一個利用全景圖半自動建構二維平面圖的方法，其目標是建立室內公共空間的平面圖。現今的數位戶外地圖已經非常普遍，但當人們進入新的建築物時也需要室內地圖，有名的建模方法像是運動恢復結構法通常需要大量和強健的特徵點來建立，但室內環境卻比較缺乏特徵點使得無法有較佳的結果，因此我們延伸建構平面圖的演算法[1]來克服這個問題。先讓使用者在全景圖上標示轉角的位置，再利用轉角與全景圖中心所形成的射線相連轉換成平面圖形，接著利用同樣方法依序產生多個平面圖形連接合成完整的平面圖；然而上述方法將無法自動的找尋相對應的牆面和被限制於曼哈頓世界假設，使無法結合成完整精確的平面圖，為了要解決上述問題，所以提出了兩大方法：牆面對應法和平面圖修整法。牆面對應法會在每兩張全景圖中所標示出的牆面找出對應，以利接下來平面圖形相連接成平面圖，我們利用幾何與圖像上的相似性來完成；在平面圖形結合完之後會因為生成與接合時留下許多錯誤，因此需要利用平面圖修整的技術來對整體的平面圖做完整的修整，我們將利用最小平方法將點和線之間距離最小化來幫助整體的修正。透過上述兩個方式，我們可以產生室內平面圖並且可建立非曼哈頓與少量特徵點的場景，最後我們利用幾組具有挑戰性的測試資料並得到與實際的平面圖相似的結果。 | zh_TW |
| dc.description.abstract | This thesis proposes a semi-automatic method that generates a 2D floorplan from cylindrical panoramic images in indoor public area. Nowadays, digital maps are common for outdoor, but people also need indoor map when entering a new building. Popular reconstruction methods like structure form motion (SfM) often need sufficient and robust features, which are lacked in many indoor environments. Our purposed method is improved by the shape generation algorithm [1] can overcome featureless conditions. The input panoramic images are separated by corner selection from a user and generate corner rays from the panorama centers to corner positions. The shape generation algorithm connects adjacent corner rays to generate shapes, and then the shapes are combined to the floorplan. Nevertheless, the said algorithm has some problems; for example, it cannot find the correspondence to combine and is limited to the Manhattan world assumption. In order to solve these problems, we present two methods: wall matching and floorplan refinement. Wall matching will correlate the wall correspondence that the wall is separated by corners. We apply the geometry and image similarity to find the best wall correspondence, and then combine the shapes to the floorplan. However, when our method computes shape combination, the error is generated from shape generation and propagates to next shape, leading overall floorplan to the inaccurate result. Therefore, we develop the corner-line minimization in least square method that is a robust floorplan refinement algorithm to get more accurate result. Finally, the method can be used to generate indoor floorplan in featureless and non-Manhattan world scene. We also demonstrate results on several challenging datasets, and the results of floorplan are similar to ground truth. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:28:36Z (GMT). No. of bitstreams: 1 ntu-104-R02922004-1.pdf: 2083466 bytes, checksum: 46e771648e83099753ab5831949c9f27 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 1.1 Background and Traditional Methods 1 1.2 Motivation 3 1.3 Overview 4 1.4 Contributions 5 1.5 Thesis Organization 5 Chapter 2 Related work 6 2.1 Image Matching 6 2.2 Floorplan Generation 7 Chapter 3 Fundamental approach 11 3.1 Manhattan World Assumption 11 3.2 Shape Generation 12 Chapter 4 Method 15 4.1 Workflow 15 4.2 Support Corner Selection Using Line Segment Detection 16 4.3 Wall Matching 17 4.3.1 Geometry Similarity 18 4.3.2 Image Similarity 19 4.3.3 Example of Wall Matching Score 20 4.4 Shape Combination 22 4.5 Floorplan Refinement 23 4.5.1 Corner-line Distance minimization 24 4.5.2 Weighted Least Square Method 27 4.5.3 Manhattan World Based Constraint 28 4.5.4 Example of floorplan refinement in non-Manhattan world scene 29 Chapter 5 Results 32 5.1 Input Data and Environment 32 5.2 Manhattan world Scene Datasets 33 5.2.1 CSIE5F 33 5.2.2 CSIE2F 36 5.2.3 Dorm 39 5.3 Non-Manhattan World Scene Datasets 42 5.3.1 CSIE1F 42 5.3.2 Math 44 Chapter 6 Discussions and Conclusion 48 6.1 Limitation 48 6.2 Conclusion 50 REFERENCE 52 | |
| dc.language.iso | en | |
| dc.title | 利用環景圖建構二維平面圖 | zh_TW |
| dc.title | 2D Floorplan Generation from Panoramic Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 賴祐吉(Yu-Chi Lai),姚智原(Chih-Yuan Yao),朱宏國(Hung-Kuo Chu),陳祝嵩(Chu-Song Chen) | |
| dc.subject.keyword | 全景圖,建構二維平面圖,曼哈頓世界假設, | zh_TW |
| dc.subject.keyword | Panoramic image,2D floorplan generation,Manhattan world assumption, | en |
| dc.relation.page | 56 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2015-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-104-1.pdf 未授權公開取用 | 2.03 MB | Adobe PDF |
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