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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電機工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78660
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dc.contributor.advisor廖婉君zh_TW
dc.contributor.advisorWanjiun Liaoen
dc.contributor.author陳與賢zh_TW
dc.contributor.authorYu-Hsien Chenen
dc.date.accessioned2021-07-11T15:10:28Z-
dc.date.available2024-08-22-
dc.date.copyright2019-08-28-
dc.date.issued2019-
dc.date.submitted2002-01-01-
dc.identifier.citation[1] J. Chakareski. Uplink scheduling of visual sensors: when view popularity matters. IEEE Transactions on Communications, 63(2):510–519, Feb 2015.
[2] C. Wang, J. Kuo, D. Yang, and W. Chen. Surveillance-aware uplink scheduling for cellular networks. IEEE Transactions on Mobile Computing, 17(12):2939–2952, Dec 2018.
[3] Booster space, beam me up - holographic telepresence using the hololens, mar. 13, 2018.
[4] Benjamin Petit, Jean-Denis Lesage, and C. Menier et al. Multicamera real-time 3D modeling for telepresence and remote collaboration. International Journal of Digital Multimedia Broadcasting, 2010, 2010.
[5] Sergio Orts-Escolano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L. Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip A. Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology, UIST ’16, pages 741–754, New York, NY, USA, 2016. ACM.
[6] A. Maimone and H. Fuchs. Encumbrance-free telepresence system with real-time 3d capture and display using commodity depth cameras. In 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pages 137–146, Oct 2011.
[7] Michal Joachimczak, Juan Liu, and Hiroshi Ando. Real-time mixed-reality telepresence via 3d reconstruction with hololens and commodity depth sensors. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, ICMI ’17, pages 514–515, New York, NY, USA, 2017. ACM.
[8] Thomas Ebner, Ingo Feldmann, Sylvain Renault, Oliver Schreer, and Peter Eisert. Multi-view reconstruction of dynamic real-world objects and their integration in augmented and virtual reality applications. Journal of the Society for Information Display, 25(3):151–157, 2017.
[9] V. A. Prisacariu, O. Kähler, D. W. Murray, and I. D. Reid. Real-time 3d tracking and reconstruction on mobile phones. IEEE Transactions on Visualization and Computer Graphics, 21(5):557–570, May 2015.
[10] P. Ondrúška, P. Kohli, and S. Izadi. Mobilefusion: Real-time volumetric surface reconstruction and dense tracking on mobile phones. IEEE Transactions on Visualization and Computer Graphics, 21(11):1251–1258, Nov 2015.
[11] Nocerino et al. 3D reconstruction with a collaborative approach based on smartphones and a cloud-based server. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pages 187–194, 11 2017.
[12] M. Pollefeys, D. Nistér, J.-M. Frahm, A. Akbarzadeh, P. Mordohai, B. Clipp, C. Engels, D. Gallup, S.-J. Kim, P. Merrell, C. Salmi, S. Sinha, B. Talton, L. Wang, Q. Yang, H. Stewénius, R. Yang, G. Welch, and H. Towles. Detailed real-time urban 3d reconstruction from video. International Journal of Computer Vision, 78(2):143–167, Jul 2008.
[13] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), volume 1, June 2006.
[14] Ricardo L. de Queiroz and Philip A. Chou. Compression of 3d point clouds using a region adaptive hierarchical transform. Trans. Img. Proc., 25(8):3947–3956, August 2016.
[15] Z. Yang, K. Nahrstedt, Y. Cui, B. Yu, J. Liang, S. Jung, and R. Bajscy. Teeve: the next generation architecture for tele-immersive environments. In Seventh IEEE International Symposium on Multimedia(ISM’05), pages 8 pp.–, Dec 2005.
[16] C. Kuster, N. Ranieri, Agustina, H. Zimmer, J. C. Bazin, C. Sun, T. Popa, and M. Gross. Towards next generation 3d teleconferencing systems. In 2012 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), pages 1–4, Oct 2012.
[17] Mike Roberts, Debadeepta Dey, Anh Truong, Sudipta Sinha, Shital Shah, Ashish Kapoor, Pat Hanrahan, and Neel Joshi. Submodular trajectory optimization for aerial 3D scanning. In International Conference on Computer Vision (ICCV) 2017, 2017.
[18] O. Mendez, S. Hadfield, N. Pugeault, and R. Bowden. Taking the scenic route to 3D: optimising reconstruction from moving cameras. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 4687–4695, Oct 2017.
[19] M. Mauro, H. Riemenschneider, A. Signoroni, R. Leonardi, and L. V. Gool. An integer linear programming model for view selection on overlapping camera clusters. In 2014 2nd International Conference on 3D Vision, volume 1, pages 464–471, Dec 2014.
[20] M. Li, C. Yeh, and S. Lu. Real-time qoe monitoring system for video streaming services with adaptive media playout. International Journal of Digital Multimedia Broadcasting, 2018(2):11, 2018.
[21] Yi Sun et al. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In Proceedings of the 2016 ACM SIGCOMM Conference, 2016.
[22] W. Huang et al. Buffer state is enough: simplifying the design of QoE-aware HTTP adaptive video streaming. IEEE Transactions on Broadcasting, 64(2):590–601, June 2018.
[23] O. El Marai, T. Taleb, M. Menacer, and M. Koudil. On improving video streaming efficiency, fairness, stability, and convergence time through client-server cooperation. IEEE Transactions on Broadcasting, 64(1):11–25, March 2018.
[24] A. Doumanoglou, D. Griffin, J. Serrano, N. Zioulis, T. K. Phan, D. Jiménez, D. Zarpalas, F. Alvarez, M. Rio, and P. Daras. Quality of experience for 3-D immersive media streaming. IEEE Transactions on Broadcasting, 64(2):379–391, June 2018.
[25] Ningqing Qian and Chao-Yang Lo. Optimizing camera positions for multi-view 3D reconstruction. In 2015 International Conference on 3D Imaging (IC3D), pages 1–8, Dec 2015.
[26] S. Shen and Z. Hu. How to select good neighboring images in depth-map merging based 3d modeling. IEEE Transactions on Image Processing, 23(1):308–318, Jan 2014.
[27] Edouard Lamboray, Stephan Wurmlin, and Markus Gross. Data streaming in telepresence environments. IEEE Transactions on Visualization and Computer Graphics, 11(6):637–348, November 2005.
[28] C. Wu. Towards linear-time incremental structure from motion. In 2013 International Conference on 3D Vision - 3DV 2013, June 2013.
[29] Shohei Mori, Momoko Maezawa, and Hideo Saito. A work area visualization by multi-view camera-based diminished reality. Multimodal Technologies and Interaction, 1(3):18, Sep 2017.
[30] Glencora Borradaile, Brent Heeringa, and Gordon Wilfong. The knapsack problem with neighbour constraints. J. of Discrete Algorithms, 16:224–235, October 2012.
[31] A. Locher, M. Perdoch, H. Riemenschneider, and L. Van Gool. Mobile phone and cloud - a dreamteam for 3D reconstruction. In 2016 IEEE Winter Conference on Applications of Computer Vision(WACV), March 2016.
[32] CPLEX optimizer, https://www.ibm.com/analytics/cplex-optimizer.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78660-
dc.description.abstract隨著諸如全息遠程呈現(Holographic telepresence)的虛擬現實(VR)的應用的出現,帶來相較於傳統多媒體系統更高的資料量傳輸,然而當前網路可能不堪負荷這些應用的高頻寬要求。另一方面,相機混合場(CBF)也被提出來恢復相機的被遮擋區域以增強用戶在VR中的體驗(QoE)。然而,之前關於有效網路資源分配的研究主要集中在傳統的單視圖,多視圖和監控視頻系統上,並不能支持新穎的VR應用。在本文中,我們利用了資源選擇,以在網路容量和3D重建的約束下來最大化用戶的QoE。我們首先制定一個新的最佳化問題,稱為在具有遮擋的實時遠程呈現中做資源選擇(SSRTO),目標是QoE最大化。接著我們證明NP-困難並提出一種新的算法,稱為最大化完整模型質量(MQCM),通過檢查我們事先建立的角度定向圖(ADG)和鄰近定向圖(NDG)來在網路容量及即時的條件之下同時最大化用戶的QoE及重建完整的3D模型。此外,我們還設計了一個偽多項式時間最優算法,稱為最佳化完整模型質量(OQCM),用於無遮擋發生的系統。根據模擬結果表明,在重構完整的三維模型時,無論是滿意度得分還是頻寬消耗,我們的演算法都能夠勝過過傳統的基本作法至少60%。zh_TW
dc.description.abstractWith the emergence of virtual reality (VR) like holographic telepresence, it is envisaged that the current networks may be overwhelmed due to the higher bandwidth demand to support high-resolution videos. On the other hand, Camera Blending Field (CBF) has been proposed to restore the occluded regions of cameras to enhance the user's Quality of Experience (QoE) in VR. However, previous works talking about effective resource allocation focused on traditional single-view, multi-view, and surveillance videos, and can not support the novel VR applications. In this paper, we explore the source selection to maximize the user's QoE under the network capacity and 3D reconstruction constraints. We first formulate a new optimization problem, named Source Selection for Real-time Telepresence with Occlusion (SSRTO), with the objective of QoE maximization. Then, we prove the NP-hardness and propose a novel algorithm, Maximum Quality of Complete Model (MQCM), to maximize the user's QoE by examining the proximity of cameras on the built Angle Directed Graph (ADG) and Neighbor Directed Graph (NDG) for reconstructing the complete 3D model. Besides, we also design a pseudo polynomial time optimal algorithm, named Optimal Quality of Complete Model (OQCM), for the occlusion-free setting. Simulation results show that our algorithms can outperform baselines no matter satisfaction score or bandwidth consumption when reconstructing complete 3D model.en
dc.description.provenanceMade available in DSpace on 2021-07-11T15:10:28Z (GMT). No. of bitstreams: 1
ntu-108-R06921082-1.pdf: 1767915 bytes, checksum: 6095b4ba40f3227f84d74f7ec47bff7f (MD5)
Previous issue date: 2019
en
dc.description.tableofcontents誌謝 i
摘要 ii
Abstract iii
Figures vi
Tables viii
Abbreviations ix
1 Introduction 1
1.1 Background 1
1.1.1 Holographic telepresence 2
1.2 Related Works 3
1.3 Motivation and Challenges 3
1.4 Thesis Organization 6
2 System Model and Problem Formulation 7
2.1 System Model 7
2.2 Integer Linear Programming Formulation 11
2.3 Prove the NP-hardness 12
3 Pseudo-polynomial time Optimal Algorithm for the Special Case 13
3.1 Algorithm OQCM 13
3.2 Complexity Analysis 16
4 Novel Algorithm for the General Case 18
4.1 Algorithm MQCM 18
4.2 Complexity Analysis 22
5 Performance Evaluation 26
5.1 Simulation Settings 26
5.2 Simulation Results for the Special Case 29
5.2.1 Scenario 1 : Number of Cameras 29
5.2.2 Scenario 2 : 3D Reconstruction Angle Upper Bound 34
5.3 Simulation Results for the General Case 35
5.3.1 Scenario 1 : Number of Cameras 35
5.3.2 Scenario 2 : 3D Reconstruction Angle Upper Bound 39
5.3.3 Scenario 3 : Percentage of the Number of Occluded Cameras 45
6 Conclusion and Future Works 49
Bibliography 50
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dc.language.isoen-
dc.subject演算法zh_TW
dc.subjectNP-困難zh_TW
dc.subject虛擬現實zh_TW
dc.subject資源選擇zh_TW
dc.subject全息遠程呈現zh_TW
dc.subjectsource selectionen
dc.subjectNP- harden
dc.subjectalgorithmen
dc.subjectVirtual Realityen
dc.subjectHolographic telepresenceen
dc.title頻寬限制下的全息遠程呈現及3D模型重建zh_TW
dc.titleBandwidth Constrained Holographic Telepresence with 3D Model Reconstructionen
dc.typeThesis-
dc.date.schoolyear107-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee楊得年;陳炳宇;王鈺強zh_TW
dc.contributor.oralexamcommitteeDe-Nian Yang;Bing-Yu Chen;Yu-Chiang Wangen
dc.subject.keyword全息遠程呈現,虛擬現實,資源選擇,演算法,NP-困難,zh_TW
dc.subject.keywordHolographic telepresence,Virtual Reality,source selection,algorithm,NP- hard,en
dc.relation.page53-
dc.identifier.doi10.6342/NTU201902731-
dc.rights.note未授權-
dc.date.accepted2019-08-10-
dc.contributor.author-college電機資訊學院-
dc.contributor.author-dept電機工程學系-
dc.date.embargo-lift2024-08-28-
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