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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47689完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊永裕 | |
| dc.contributor.author | Ting-Tzu Chang | en |
| dc.contributor.author | 張庭慈 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:12:49Z | - |
| dc.date.available | 2010-08-16 | |
| dc.date.copyright | 2010-08-16 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-12 | |
| dc.identifier.citation | [1] Bing Maps. http://www.bing.com/maps.
[2] A. Agarwala, M. Agrawala, M. Cohen, D. Salesin1, and R. Szeliski. Photographing long scenes with multi-viewpoint panoramas. Proceedings of SIGGRAPH, 25(3):853–861, 2006. [3] A. Agarwala, C. Zheng, C. Pal, M. Agarwala, M. Cohen, B. Curless, D. Salesin, and R. Szeliski. Panoramic video textures. Proceedings of SIGGRAPH, 24(3):821–827, 2005. [4] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. Surf: Speeded up robust features. Computer Vision and Image Understanding, 110:346–359, 2008. [5] B. Chen, B. Neubert, E. Ofek, O. Deussen, and M. F. Cohen. Integrated videos and maps for driving directions. User Interface Science and Technology, 2009. [6] R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, second edition, 2004. [7] R. I. Hartley and R. Gupta. Linear pushbroom cameras. IEEE Transaction on Pattern Analysis and Machine Intelligence, 19(9):963–975, 1997. [8] M. Havlena, A. Torii, and T. Pajdla. Randomized structure from motion based on atomic 3d models from camera triplets. IEEE Conference on Computer Vision and Pattern Recognition, 2009. [9] P. J. Huber. Robust Statistics. John Wiley, 1981. [10] M. Koller. seamless city - San Francisco. http://www.seamlesscity.com/. 39 [11] J. Kopf, B. Chen, R. Szeliski, and M. F. Cohen. Street slide: Browsing street level imagery. Proceedings of SIGGRAPH, 29(4):853–861, 2010. [12] D. G. LOWE. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91–110, 2004. [13] B. Micusik and J. Kosecka. Piecewise planar city 3d modeling from street view panoramic sequences. IEEE Conference on Computer Vision and Pattern Recognition, 2009. [14] J.-M. Morel and G. Yu. Asift: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences, 2(2):438–469, 2009. [15] D. Nister. An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:756–770, 2004. [16] A. Rav-Acha, G. Engel, and S. Peleg. Minimal Aspect Distortion (MAD) Mosaicing of Long Scenes. International Journal of Computer Vision, 78(2-3):187–206, 2007. [17] A. Roman, G. Garg, and M. Levoy. Interactive design of multi-perspective images for visualizing urban landscapes. Proceedings of IEEE Visualization., page 537–544, 2004. [18] A. Roman and H. P. Lensch. Automatic multiperspective images. Proceedings of Eurographics Symposium on Rendering., page 161–171, 2006. [19] R. Szeliski and H.-Y. Shum. Creating full view panoramic image mosaics and environment maps. Proceedings of SIGGRAPH, pages 251–258, 1997. [20] J.-P. Tardif, Y. Pavlidis, and K. Daniilidis. Monocular visual odometry in urban environments using an omnidirectional camera. International Conference on Intelligent Robots and Systems, 2008. [21] A. Torii, M. Havlena, and T. Pajdla. From google street view to 3d city models. OMNIVIS, 2009. 40 [22] A. Torii, M. Havlena, and T. Pajdla. Omnidirectional image stabilization by computing camera trajectory. PSIVT, 5414:71–82, 2009. [23] A. Zomet, D. Feldman, and S. Peleg. Mosaicing new views: The crossed-slits projection. IEEE Transaction on Pattern Analysis and Machine Intelligence, 25(6):741– 754, 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47689 | - |
| dc.description.abstract | 谷歌街景(Google Street View)現在提供了一個街道瀏覽系統給使用者線上使用，可瀏覽街道遍及全世界大部分區域。系統利用全方向的影像(omnidirectional images)建立一個擬真的360度環景泡泡(bubble)為使用者帶來如臨其境的虛擬行走感。然而，由於使用者在泡泡中行為受到限制以及在泡泡間移動為不連續跳動，系統並沒有辦法為較長的街道提供一個好的視覺摘要。
因此，本篇論文提出一個新的系統呈現谷歌街景環景視覺化。系統只需要使用者輸入起點和終點的住址，便會自動擷取谷歌街景的資料、經由SFM(Struture From Motion)找出路段的立體模型、並用不密集的連續全方向圖為資料藉由圖割(Graph-Cut)最小化目標方程式產生出多視點環景圖(Multi-Viewpoint Panorama)。我們將證明我們的結果相當有用，只需讓使用者看一眼，便可簡單快速得到一段長路程視覺摘要。 | zh_TW |
| dc.description.abstract | Nowadays, Google Street View provides user a street navigating system online available in many areas over the world. The system brings photorealism virtual visit sense by constructing immersive $360^{circ}$ panorama or bubble using omnidirectional images. However, it does not provide a good summary vision of the long street due to its limited action and discretely jumping from bubble to bubble.
As a result, we bring a system presenting Google Street View panoramic visualization. Once user input addresses of the starting point and the goal, the system automatically fetching data from Google Street View, recovering 3D models through SFM framework, and producing multi-viewpoint panorama from these sparse omnidirectional consecutive images by minimizing objective function using Graph-Cut. We show that our result is useful for user to easily and rapidly retrieve a visual summary just one glance of long scene. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:12:49Z (GMT). No. of bitstreams: 1 ntu-99-R97922035-1.pdf: 34520656 bytes, checksum: bde58a55059d69289ed9f967711bb6a1 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 口試委員會審定書i
致謝ii 中文摘要iv Abstract v 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 RelatedWork 3 2.1 Omnidirectional Images . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Multi-Viewpoint Panorama . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 System Overview 5 4 Google Street View Data Retrieve 7 4.1 Google Street View Introduction . . . . . . . . . . . . . . . . . . . . . . 7 4.2 Google Street View Images Online . . . . . . . . . . . . . . . . . . . . . 7 4.3 Fetching Every Panorama on the Route . . . . . . . . . . . . . . . . . . 8 4.3.1 Google Direction Service . . . . . . . . . . . . . . . . . . . . . . 9 4.3.2 Traverse Along the Route . . . . . . . . . . . . . . . . . . . . . 11 5 Structure From Motion 13 5.1 Feature Matching on Omnidirectional Images . . . . . . . . . . . . . . . 13 5.1.1 Projecting Images . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5.1.2 ASIFT Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5.2 Structure From Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.2.1 Transformation of Coordinate System . . . . . . . . . . . . . . . 16 5.2.2 Refining Matching Pairs and Finding Essential Matrix Through RANSAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 5.2.3 Camera Pose Estimation by Epipolar Geometry . . . . . . . . . . 18 6 Image Based Rendering 22 6.1 Picture Surface Selection . . . . . . . . . . . . . . . . . . . . . . . . . . 22 6.1.1 User Defined Picture Surface . . . . . . . . . . . . . . . . . . . . 23 6.1.2 Automatically Defined Picture Surface . . . . . . . . . . . . . . 23 vi 6.2 Viewpoint Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 7 Experiment Results 28 7.1 Weights Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 7.2 User and Automatically Picture Surface Selection . . . . . . . . . . . . . 28 7.3 More Panorama Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 7.4 Failure Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 7.4.1 Feature Matching Stage . . . . . . . . . . . . . . . . . . . . . . 29 7.4.2 Image Based Rendering Stage . . . . . . . . . . . . . . . . . . . 29 8 Conclusion and FutureWork 38 8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 8.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Bibliography 39 | |
| dc.language.iso | en | |
| dc.subject | 谷歌街景 | zh_TW |
| dc.subject | 圖割 | zh_TW |
| dc.subject | Structure from Motion(SFM) | zh_TW |
| dc.subject | 全方向圖 | zh_TW |
| dc.subject | 環景圖 | zh_TW |
| dc.subject | Graph-Cut | en |
| dc.subject | Google Street View | en |
| dc.subject | Structure from Motion(SFM) | en |
| dc.subject | Panorama | en |
| dc.subject | Omnidirectional Images | en |
| dc.title | 谷歌街景圖之長場景全景視覺化 | zh_TW |
| dc.title | Long-Scene Panoramic Visualization for Google Street View Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳文進,周承復 | |
| dc.subject.keyword | 谷歌街景,Structure from Motion(SFM),環景圖,全方向圖,圖割, | zh_TW |
| dc.subject.keyword | Google Street View,Structure from Motion(SFM),Panorama,Omnidirectional Images,Graph-Cut, | en |
| dc.relation.page | 41 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-13 | |
| 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-99-1.pdf 未授權公開取用 | 33.71 MB | Adobe PDF |
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