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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳良基 | |
| dc.contributor.author | Yu-Sheng Hsu | en |
| dc.contributor.author | 徐佑昇 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:35:28Z | - |
| dc.date.copyright | 2019-08-01 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-30 | |
| dc.identifier.citation | [1] M. Firman, “Rgbd datasets: Past, present and future,” in Proceedings
of the IEEE conference on computer vision and pattern recognition workshops, pp. 19–31, 2016. [2] A. Criminisi, P. Pérez, and K. Toyama, “Region filling and object re- moval by exemplar-based image inpainting,” IEEE Transactions on image processing, vol. 13, no. 9, pp. 1200–1212, 2004. [3] M. Dou, S. Khamis, Y. Degtyarev, P. Davidson, S. R. Fanello, A. Kow- dle, S. O. Escolano, C. Rhemann, D. Kim, J. Taylor, et al., “Fusion4d: Real-time performance capture of challenging scenes,” ACM Transac- tions on Graphics (TOG), vol. 35, no. 4, p. 114, 2016. [4] E. Bosc, M. Pressigout, and L. Morin, “Focus on visual rendering qual- ity through content-based depth map coding,” in 28th Picture Coding Symposium, pp. 158–161, IEEE, 2010. [5] F. Ebrahimi, M. Chamik, and S. Winkler, “Jpeg vs. jpeg 2000: an objective comparison of image encoding quality,” in Applications of Digital Image Processing XXVII, vol. 5558, pp. 300–309, International Society for Optics and Photonics, 2004. [6] Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE signal processing letters, vol. 9, no. 3, pp. 81–84, 2002. [7] R. Hartley and A. Zisserman, Multiple view geometry in computer vi- sion. Cambridge university press, 2003. [8] R. Lange and P. Seitz, “Solid-state time-of-flight range camera,” IEEE Journal of quantum electronics, vol. 37, no. 3, pp. 390–397, 2001. [9] D. Scharstein and R. Szeliski, “High-accuracy stereo depth maps us- ing structured light,” in 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., vol. 1, pp. I–I, IEEE, 2003. [10] R. I.-R. BT, “Methodology for the subjective assessment of the quality of television pictures,” 2002. [11] P. ITU-T RECOMMENDATION, “Subjective video quality assess- ment methods for multimedia applications,” International telecommu- nication union, 1999. [12] M. Bertalmio, A. L. Bertozzi, and G. Sapiro, “Navier-stokes, fluid dy- namics, and image and video inpainting,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I–I, IEEE, 2001. [13] K.-J. Oh, S. Yea, and Y.-S. Ho, “Hole-filling method using depth based in-painting for view synthesis in free viewpoint television (ftv) and 3d video,” in Picture Coding Symposium, 2009. [14] I. Daribo and B. Pesquet-Popescu, “Depth-aided image inpainting for novel view synthesis,” in 2010 IEEE International Workshop on Mul- timedia Signal Processing, pp. 167–170, IEEE, 2010. [15] P.-K. Tsung, P.-C. Lin, L.-F. Ding, S.-Y. Chien, and L.-G. Chen, “Sin- gle iteration view interpolation for multiview video applications,” in 2009 3DTV Conference: The True Vision-Capture, Transmission and Display of 3D Video, pp. 1–4, IEEE, 2009. [16] L. Zhang and W. Tam, “Stereoscopic image generation based on depth images for 3d tv,” Broadcasting, IEEE Transactions on, vol. 51, pp. 191 – 199, 07 2005. [17] Z. Tauber, Z. Li, and M. S. Drew, “Review and preview: Disocclu- sion by inpainting for image-based rendering,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, pp. 527–540, July 2007. [18] S. Zinger, L. Do, and P. de With, “Free-viewpoint depth image based rendering,” Journal of visual communication and image representation, vol. 21, no. 5-6, pp. 533–541, 2010. [19] M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, “Simultaneous struc- ture and texture image inpainting,” IEEE transactions on image pro- cessing, vol. 12, no. 8, pp. 882–889, 2003. [20] M. Tanimoto, T. Fujii, K. Suzuki, N. Fukushima, and Y. Mori, “Ref- erence softwares for depth estimation and view synthesis, iso,” tech. rep., IEC JTC1/SC29/WG11MPEG2008, 2008. [21] M. Tanimoto, T. Fujii, and K. Suzuki, “View synthesis algo- rithm in view synthesis reference software 2.0 (vsrs2. 0),” ISO/IEC JTC1/SC29/WG11 M, vol. 16090, p. 2009, 2009. [22] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, et al., “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004. [23] I. Daribo and H. Saito, “A novel inpainting-based layered depth video for 3dtv,” IEEE Transactions on Broadcasting, vol. 57, no. 2, pp. 533– 541, 2011. [24] I. Ahn and C. Kim, “A novel depth-based virtual view synthesis method for free viewpoint video,” IEEE Transactions on Broadcasting, vol. 59, no. 4, pp. 614–626, 2013. [25] C. Yao, T. Tillo, Y. Zhao, J. Xiao, H. Bai, and C. Lin, “Depth map driven hole filling algorithm exploiting temporal correlation informa- tion,” IEEE Transactions on Broadcasting, vol. 60, no. 2, pp. 394–404, 2014. [26] G. Luo, Y. Zhu, Z. Li, and L. Zhang, “A hole filling approach based on background reconstruction for view synthesis in 3d video,” in Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1781–1789, 2016. [27] C. L. Zitnick, S. B. Kang, M. Uyttendaele, S. Winder, and R. Szeliski, “High-quality video view interpolation using a layered representation,” in ACM transactions on graphics (TOG), vol. 23, pp. 600–608, ACM, 2004. [28] C. Zhu and S. Li, “Depth image based view synthesis: New insights and perspectives on hole generation and filling,” IEEE Transactions on Broadcasting, vol. 62, no. 1, pp. 82–93, 2015. [29] G. Luo and Y. Zhu, “Hole filling for view synthesis using depth guided global optimization,” IEEE Access, vol. 6, pp. 32874–32889, 2018. [30] C.-M. Cheng, S.-J. Lin, S.-H. Lai, and J.-C. Yang, “Improved novel view synthesis from depth image with large baseline,” in 2008 19th International Conference on Pattern Recognition, pp. 1–4, IEEE, 2008. [31] P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, “Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor en- vironments,” The International Journal of Robotics Research, vol. 31, no. 5, pp. 647–663, 2012. [32] K. Lai, L. Bo, X. Ren, and D. Fox, “A large-scale hierarchical multi- view rgb-d object dataset,” in 2011 IEEE international conference on robotics and automation, pp. 1817–1824, IEEE, 2011. [33] K. Lai, L. Bo, X. Ren, and D. Fox, “Detection-based object labeling in 3d scenes,” in 2012 IEEE International Conference on Robotics and Automation, pp. 1330–1337, IEEE, 2012. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21485 | - |
| dc.description.abstract | With the rapid improvement of technology, RGB/D cameras have been growing more and more popular. Depth map plays an important role for many 3D applications in human–computer interaction systems. There are several technical challenges in producing a high-quality synthesized view. To generate more comfortable novel view has become the bottleneck for current research.
Multimedia applications such as 3DTV, and virtual reality provide viewers with 3D experience by presenting videos from different viewpoints to our eyes. Through we can build rich 3D maps of environments is an important task for mobile robotics, with applications in navigation, manipulation, semantic mapping, and telepresence. But 3D point clouds frame-to-frame alignment and dense 3D reconstruction require high bandwidth, memory and computational costs because of costly iterative operations, the original point cloud is computationally expensive for real-time system implementation. View synthesis is just like 3D information projection to 2D, is an efficient implementation in daily life. Most popular view synthesis system adopted by the ITU/MPEG standard uses depth image based rendering (DIBR) techniques. In this thesis, we propose to tackle the artifacts, pinhole, disocclusion of RGB-D multiview images when synthesizing new views of a scene by changing its viewpoint. We first examine how ghost contour come from, why the disocclusion region cannot be seen in the original view but exposed in the virtual view, and pinholes/Cracks appear in the derived frame for surfaces whose normal has rotated towards the user in the derived frame. 2D information from images transformation to 3D and make connection between reference view and novel view. We analyze 3D warping techniques: background erosion is proposed here to remove the wrongly warped boundary, forward warping is to write a single derived pixel for each warped reference pixel. And we define a vector displacement as corresponding feature points’ “movement” between different views to help us to do backward warping. Further more, we point out the disocclusion is the area between foreground-background edge displacement difference, and we combine convention inpainting technique and our growing guidance to get improvement on hole filling. Large holes are pretty hard to be filled with an acceptable subjective quality. The situation can be relieved by multiview. Despite of z-buffering, we also propose view weighting based on the distance between reference and novel view and the other method winner take all. In order to generate free view, we exploit quaternion rotation to do inter-view interpolation, and analyze the quality versus view point. Finally, we can provide quality good and comfortable virtual view synthesis through our proposed. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:35:28Z (GMT). No. of bitstreams: 1 ntu-108-R04943008-1.pdf: 25770046 bytes, checksum: 673bcafdb935c48e6fc08d52af392b26 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | Contents
Abstract xi 1 Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 3D Capturing Apparatus . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . 5 2 Challenges of Free-Viewpoint View Synthesis 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 View Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Pose Synthesis for Novel View: Quatrotation . . . . . . . . . 17 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 View Synthesis for Single View 27 3.1 System overview . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Pre-processing for Depth Map . . . . . . . . . . . . . . . . . 28 3.3 3D Warping . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Hybrid Depth/Displacement guided Inpainting for Virtual View Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4 View Synthesis for Multiview 47 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2 View Blending . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 View Blending for more than Two Views . . . . . . . . . . . 49 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 5 Analysis of Proposed View Synthesis 55 5.1 Visual Quality Evaluation . . . . . . . . . . . . . . . . . . . 55 5.2 Analysis of View Synthesis for Single View . . . . . . . . . . 59 5.3 Analysis of View Synthesis for MultiView . . . . . . . . . . 63 5.4 Analysis of Background Erosion . . . . . . . . . . . . . . . . 67 5.5 Analysis of Backward Warping . . . . . . . . . . . . . . . . 68 5.6 Analysis of Hybrid Depth/Displacement guided Inpainting . 73 5.7 Analysis of View Synthesis for Multiview . . . . . . . . . . . 75 5.8 Analysis of Real Data . . . . . . . . . . . . . . . . . . . . . 80 6 Conclusion 87 6.1 Principal Contributions . . . . . . . . . . . . . . . . . . . . 87 6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . 88 6.2.1 True Free View View Synthesis for Multiview . . . . 88 6.2.2 Real-time for Multiview View Synthesis . . . . . . . 88 Bibliography 89 | |
| dc.language.iso | en | |
| dc.title | 基於位移導向之立體影像視角合成應用於單一或多視角彩色深度相機 | zh_TW |
| dc.title | Displacement-oriented View Synthesis for Single/Multiple RGBD Cameras | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 簡韶逸,楊佳玲,黃俊郎 | |
| dc.subject.keyword | 影像處理,相機,影像,視角合成,多視角,深度,位移, | zh_TW |
| dc.subject.keyword | camera,RGBD,image processing,view synthesis,displacement,depth,multiview, | en |
| dc.relation.page | 93 | |
| dc.identifier.doi | 10.6342/NTU201902139 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| 顯示於系所單位: | 電子工程學研究所 | |
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