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Toward Non-rigid Dynamic Cage Capture

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Cage-based Performance Capture

Part of the book series: Studies in Computational Intelligence ((SCI,volume 509))

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Abstract

This chapter explores the problem of non-rigid alignment using cage-based parametrization to obtain consistent dynamic meshes without any assumptions on temporal matching.

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References

  1. J. Gallego, J. Salvador, J.R. Casas, M. Pardàs, Joint multi-view foreground segmentation and 3D reconstruction with tolerance loop, in 18th IEEE International Conference on Image Processing (2011), pp. 997–1000

    Google Scholar 

  2. B. Goldlucke, M.A. Magnor, Joint 3D-reconstruction and background separation in multiple views using graph cuts, in CVPR (2003), pp. 683–694

    Google Scholar 

  3. M. Gong, L. Cheng, Real-time foreground segmentation on gpus using local online learning and global graph cut optimization, in CPR (2008), pp. 1–4

    Google Scholar 

  4. A. Laurentini, The visual hull concept for silhouette-based image understanding. IEEE Trans. Pattern Anal. Mach. Intell. 16(2), 150–162 (1994)

    Google Scholar 

  5. E. Aganj, J-P. Pons, F. Ségonne, R. Keriven. Spatio-temporal shape from silhouette using four-dimensional delaunay meshing, in ICCV (2007), pp. 1–8

    Google Scholar 

  6. W. Matusik, C. Buehler, L. McMillan, Polyhedral visual hulls for real-time rendering, in Proceedings of the 12th Eurographics Workshop on Rendering, Techniques (2001), pp. 115–126

    Google Scholar 

  7. S. Yous, H. Laga, M. Kidode, K. Chihara. Gpu-based shape from silhouettes, in Graphite ’07 (2007), pp. 71–77

    Google Scholar 

  8. R. Szeliski. Rapid octree construction from image sequences. CVGIP: Image Underst. 58(1), 23–32 (1993)

    Google Scholar 

  9. K. Müller, A. Smolic, B. Kaspar, T. Rein, P. Eisert, Octree voxel modeling with multi-view texturing in cultural heritage scenarios, (2004)

    Google Scholar 

  10. A. Erol, G. Bebis, R.D. Boyle, M. Nicolescu, Visual hull construction using adaptive sampling, in WACV-MOTION ’05 (2005), pp. 234–241

    Google Scholar 

  11. D. Knoblauch, F. Kuester, Focused volumetric visual hull with color extraction, in Proceedings of the 5th International Symposium on Advances in Visual Computing, ISVC (2009), pp. 208–217

    Google Scholar 

  12. D. Knoblauch, F. Kuester, Region-of-interest volumetric visual hull refinement, in VRST ’10 (2010), pp. 143–150

    Google Scholar 

  13. W. Matusik, C. Buehler, R. Raskar, S.J. Gortler, L. McMillan, Image-based visual hulls, in SIGGRAPH ’00 (2000), pp. 369–374

    Google Scholar 

  14. W. Matusik, C. Buehler, L. McMillan, S.J. Gortler, An efficient visual hull computation algorithm. Technical report (2002), pp. 1–5

    Google Scholar 

  15. J.R. Isidoro, Stochastic mesh-based multiview reconstruction. Ph.D. thesis (2004)

    Google Scholar 

  16. K. Grauman, G. Shakhnarovich, T. Darrell, A bayesian approach to image-based visual hull reconstruction, in Proceedings of the, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’ 03) (2003)

    Google Scholar 

  17. D. Snow, P. Viola, R. Zabih, Exact voxel occupancy with graph cuts, in CVPR (2000), pp. 345–352

    Google Scholar 

  18. A. Ladikos, S. Benhimane, N. Navab, Efficient visual hull computation for real-time 3d reconstruction using cuda, in Proceedings of the 2008 Conference on Computer Vision and Pattern Recognition Workshops (2008)

    Google Scholar 

  19. H. Kim, M. Sarim, T. Takai, J.-Y. Guillemaut, A. Hilton, Dynamic 3d scene reconstruction in outdoor environments, in 3DPVT (2010)

    Google Scholar 

  20. B. Mercier, D. Meneveaux, Shape from silhouette: Image pixels for marching cubes. J. WSCG’2005 13, 112–118 (2005)

    Google Scholar 

  21. P. Milne, F. Nicolls, G. Jager, Visual hull surface estimation, in PRASA (2004)

    Google Scholar 

  22. C. Liang, K.-Y. Kenneth Wong, Exact visual hull from marching cubes, in VISAPP, Institute for Systems and Technologies of Information, Control and, Communication (INSTICC) (2008), pp. 597–604

    Google Scholar 

  23. J. Salvador, X. Suau, J.R. Casas, From silhouettes to 3d points to mesh: towards free viewpoint video, in 3DVP (2010), pp. 19–24

    Google Scholar 

  24. A. Hornung, L. Kobbelt, Robust reconstruction of watertight 3d models from non-uniformly sampled point clouds without normal information, in SGP ’06 (2006), pp. 41–50

    Google Scholar 

  25. M. Kazhdan, A. Klein, K. Dalal, H. Hoppe, Unconstrained isosurface extraction on arbitrary octrees, in SGP (2007), pp. 125–133

    Google Scholar 

  26. M. Sormann, C. Zach, J. Bauer, K. Karner, H. Bishof, Watertight multi-view reconstruction based on volumetric graph-cuts, in SCIA (2007), pp. 393–402

    Google Scholar 

  27. Z. J. Wood, P. Schröder, D. Breen, M. Desbrun, Semi-regular mesh extraction from volumes, in Proceedings of the Conference on Visualization (2000), pp. 275–282

    Google Scholar 

  28. B. Goldlucke, M.A. Magnor, Space-time isosurface evolution for temporally coherent 3d reconstruction, in CVPR (2004), pp. 350–355

    Google Scholar 

  29. J. Sreevalsan-Nair, L. Linsen, B. Hamann, Topologically accurate dual isosurfacing using ray intersection. J. Virtual Reality Broadcast. 4(4), 12 (2007)

    Google Scholar 

  30. A.A. Montenegro, L. Velho, P.C.P. Carvalho, J. Jr. Sossai, Polygonization of volumetric reconstructions from silhouettes, in SIBGRAPI (2006)

    Google Scholar 

  31. H. Hoppe, T. DeRose, T. Duchamp, M. Halstead, H. Jin, J. McDonald, J. Schweitzer, W. Stuetzle, Piecewise smooth surface reconstruction, in SIGGRAPH ’94 (1994)

    Google Scholar 

  32. M. Goesele, B. Curless, S.M. Seitz, Multi-view stereo revisited, in CVPR ’06 (2006), pp. 2402–2409

    Google Scholar 

  33. Y. Liu, Q. Dai, W. Xu, A point-cloud-based multiview stereo algorithm for free-viewpoint video. IEEE Trans. Visual Comput. Graph. 16(3), 407–18 (2010)

    Google Scholar 

  34. T. Tung, S. Nobuhara, T. Matsuyama, Simultaneous super-resolution and 3d video using graph-cuts, in CVPR (2008)

    Google Scholar 

  35. H. Schirmacher, M. Li, M.A. Magnor, H.-P. Seidel, Combining stereo and visual hull information for on-line reconstruction and rendering of dynamic scenes, in IEEE Workshop on Multimedia, Signal Processing (2002), pp. 9–12

    Google Scholar 

  36. C.H. Esteban, F. Schmitt, Silhouette and stereo fusion for 3d object modeling, in Fourth International Conference on 3D Digital Imaging and Modeling (2004), pp. 46–54

    Google Scholar 

  37. Y. Liu, G. Chen, N. Max, C. Hofsetz, P. McGuinness. Visual hull rendering with multi-view stereo refinement, in WSCG’04 (2004), pp. 261–268

    Google Scholar 

  38. P. Song, X. Wu, M.Y. Wang, Volumetric stereo and silhouette fusion for image-based modeling. Vis. Comput. 26, 1435–1450 (2010)

    Google Scholar 

  39. C. Wu, Y. Liu, Q. Dai, B. Wilburn, Fusing multiview and photometric stereo for 3d reconstruction under uncalibrated illumination. IEEE Trans. Vis. Comput. Graph. 17, 1082–1095 (2011)

    Google Scholar 

  40. Y. Liu, Q. Dai, W. Xu, Graph-cuts fusion of distance fidelity maps for volumetric multi-view stereo. J. Electron. (China) 18(3), 449–454 (2009)

    Google Scholar 

  41. S. Tran, L.S. Davis, 3d surface reconstruction using graph cuts with surface constraints, in ECCV (2006), pp. 219–231

    Google Scholar 

  42. C.H. Esteban, F. Schmitt, A snake approach for high quality image-based 3d object modeling, in Variational, Geometric and Level Set Methods in Computer Vision (2003), pp. 241–248

    Google Scholar 

  43. C.H. Esteban, F. Schmitt, Silhouette and stereo fusion for 3d object modeling. Comput. Vis. Image Underst. 96(3), 367–392 (2004)

    Google Scholar 

  44. S. Paris, F.X. Sillion, L. Quan, A surface reconstruction method using global graph cut optimization. Int. J. Comput. Vision 66(21), 41–161 (2004)

    Google Scholar 

  45. G. Vogiatzis, C. Hernandez, R. Cipolla, Reconstruction in the round using photometric normals and silhouettes, in CVPR (2006), pp. 1847–1854

    Google Scholar 

  46. G. Vogiatzis, P.H.S. Torr, R. Cipolla, Multi-view stereo via volumetric graph-cuts (2005)

    Google Scholar 

  47. A. Ladikos, S. Benhimane, N. Navab, Multi-view reconstruction using narrow-band graph-cuts and surface normal optimization, in BMVC (2008)

    Google Scholar 

  48. P. Paalanen, J.-K. Kamarainen, Narrow baseline GLSL multiview stereo, in 3DPVT (2010)

    Google Scholar 

  49. S.N. Sinha, P. Mordohai, M. Pollefeys, Multi-view stereo via graph cuts on the dual of an adaptive tetrahedral mesh, in ICCV 2007 (2007)

    Google Scholar 

  50. A. Hornung, L. Kobbelt, Hierarchical volumetric multi-view stereo reconstruction of manifold surfaces based on dual graph embedding, in CVPR ’06 (2006), pp. 503–510

    Google Scholar 

  51. F. Cuzzolin, A. Sarti, S. Tubaro, Invariant action classification with volumetric data (2005)

    Google Scholar 

  52. S. Y. Cheng, M.M. Trivedi, Hand pose estimation using expectation-constrained-maximization from voxel data, in CVRR, (Technical report) (2005)

    Google Scholar 

  53. E. de Aguiar, C. Theobalt, M. Magnor, H. Theisel, H.-P. Seidel, M3 : Marker-free model reconstruction and motion tracking from 3d voxel data (2004)

    Google Scholar 

  54. E. de Aguiar, C. Theobalt, M. Magnor, H.P. Seidel, Reconstructing human shape and motion from multi-view video, in CVMP (2005), pp. 42–49

    Google Scholar 

  55. K. Li, Q. Dai, W. Xu, Markerless shape and motion capture from multi-view video sequences. IEEE Trans. Circuits Syst. Video Technol. 21(3), 320–334 (2011)

    Google Scholar 

  56. B. Berendsen, X. Luo, W. Hürst, R.C. Veltkamp, Volumetric modeling of 3d human pose from multiple video (2007)

    Google Scholar 

  57. S. Ando, W. Xiaojun, A. Suzuki, K. Wakabayashi, H. Koike, Human pose estimation for image monitoring (Image Processing Technologies for Image Monitoring Services), in Special Feature (2007)

    Google Scholar 

  58. J. Starck, A. Hilton, Model-based multiple view reconstruction of people, in ICCV ’03 (2003)

    Google Scholar 

  59. C. Curio, M.A. Giese, Combining view-based and model-based tracking of articulated human movements, in WACV-MOTION (2005)

    Google Scholar 

  60. F. Caillette, A. Galata, T. Howard, Real-time 3-d human body tracking using learnt models of behaviour. Comput. Vis. Image Underst. 109, 112–125 (2008)

    Google Scholar 

  61. B. Michoud, E. Guillou, H.B. Pulido, S. Bouakaz, Real-time marker-free motion capture from multiple cameras, in ICCV (2007)

    Google Scholar 

  62. T. Yang, Y. Zhang, M. Li, D. Shao, X. Zhang, A multi-camera network system for markerless 3d human body voxel reconstruction, in ICIG ’09 (2009), pp. 706–711

    Google Scholar 

  63. P.J. Besl, N.D. McKay, A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Google Scholar 

  64. S. Rusinkiewicz, Marc Levoy, Efficient variants of the ICP algorithm, in Third International Conference on 3D Digital Imaging and Modeling (3DIM) (2001)

    Google Scholar 

  65. A. Segal, D. Haehnel, S. Thrun, Generalized-icp science and systems, in Proceedings of Robotics (2009)

    Google Scholar 

  66. T. Jost, H. Hügli, Fast icp algorithms for shape registration, in Proceedings of the 24th DAGM Symposium on, Pattern Recognition (2002), pp. 91–99

    Google Scholar 

  67. S. Corazza, L. Mündermann, E. Gambaretto, G. Ferrigno, T.P. Andriacchi, Markerless motion capture through visual hull, articulated icp and subject specific model generation. Int. J. Comput. Vision 1–2, 156–169 (2010)

    Google Scholar 

  68. W. Luo, T. Yamasaki, K. Aizawa, Articulated human motion capture from segmented visual hulls and surface reconstruction, in APSIPA (Annual summit and conference) (2010)

    Google Scholar 

  69. S. Pellegrini, K. Schindler, D. Nardi, A generalisation of the icp algorithm for articulated bodies, in BMVC (2008)

    Google Scholar 

  70. D. Anguelov, D. Koller, P. Srinivasan, S. Thrun, H.-C. Pang, and J. Davis, The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces, in Advances in Neural Information Processing Systems (NIPS 2004) (2005)

    Google Scholar 

  71. D. Anguelov, D. Koller, H.-C. Pang, P. Srinivasan, S. Thrun, Recovering articulated object models from 3d range data, in Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI ’04 (2004)

    Google Scholar 

  72. W. Chang, M. Zwicker, Automatic registration for articulated shapes, in Computer Graphics Forum (Proceedings of SGP 2008) (2008)

    Google Scholar 

  73. Y. Pekelny, C. Gotsman, Articulated object reconstruction and markerless motion capture from depth video. Comput. Graph. Forum 27(2), 399–408 (2008)

    Google Scholar 

  74. Q. Zheng, A. Sharf, A. Tagliasacchi, B. Chen, H. Zhang, A. Sheffer, D. Cohen-Or, Consensus skeleton for non-rigid space-time registration. Comput. Graph.Forum (Special Issue of Eurographics) 29(2), 635–644 (2010)

    Google Scholar 

  75. W. Chang, M. Zwicker, Global registration of dynamic range scans for articulated model reconstruction. ACM Trans. Graph. 30, 26:1–26:15 (2011)

    Google Scholar 

  76. L. Sigal, A.O. Balan, M.J. Black, Combined discriminative and generative articulated pose and non-rigid shape estimation, In NIPS’07 (2007)

    Google Scholar 

  77. L.-J. Chu, C.-P. Chen, C.-S. Chen, Y.-P. Hung, 3d human motion tracking with soft-joint constrained icp, in IPPR Conference on Computer Vision Graphics and Image Processing (CVGIP) (2008)

    Google Scholar 

  78. S. Arya, D.M. Mount, N.S. Netanyahu, R. Silverman, A.Y. Wu, An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 89—923 Nov (1998)

    Google Scholar 

  79. M. Greenspan, G. Godin, A nearest neighbor method for efficient ICP, in Proceedings of Third International Conference on 3D Digital Imaging and Modeling (2001), pp. 161–168

    Google Scholar 

  80. M. Connor, P. Kumar, Fast construction of k-nearest neighbor graphs for point clouds. IEEE Trans. Visual Comput. Graphics 16(4), 599–608 (2010)

    Google Scholar 

  81. K.-L. Low, Linear least-squares optimization for point-to-plane icp surface registration (2004)

    Google Scholar 

  82. J. Starck, A. Hilton, Spherical matching for temporal correspondence of non-rigid surfaces, in ICCV (2005), pp. 1387–1394

    Google Scholar 

  83. T. Tung, T. Matsuyama, Dynamic surface matching by geodesic mapping for 3d animation transfer, in CVPR (2010)

    Google Scholar 

  84. N. Ahmed, C. Theobalt, C. Rössl, S. Thrun, H.-P. Seidel, Dense correspondence finding for parametrization-free animation reconstruction from video, in CVPR(2008)

    Google Scholar 

  85. A. Doshi, J. Starck, A. Hilton, An empirical study of non-rigid surface feature matching of human from 3d video. J. Virtual Reality Broadcast. 7(3), 1–11 (2010)

    Google Scholar 

  86. A. Doshi, A. Hilton, J. Starck, An empirical study of non-rigid surface feature matching, in CVMP (2008)

    Google Scholar 

  87. O. Enqvist, K. Josephson, F. Kahl, Optimal correspondences from pairwise constraints, in IEEE 12th International Conference on Computer Vision (2009), pp. 1295–1302

    Google Scholar 

  88. L. Zhang, S.-I. Choi, S.-Y. Park, Robust icp registration using biunique correspondence, in 3DIMPVT (2011), pp. 80–85

    Google Scholar 

  89. C. Stoll, Z. Karni, C. Rössl, H. Yamauchi, H.-P. Seidel, Template deformation for point cloud fitting, in SPBG (2006), pp. 27–35

    Google Scholar 

  90. I.-C. Yeh, C.-H. Lin, O. Sorkine, T.-Y. Lee, Template-based 3d model fitting using dual-domain relaxation. IEEE Trans. Visual Comput. Graphics 99, 1178–1190 (2010)

    Google Scholar 

  91. N. Hasler, C. Stoll, B. Rosenhahn, T. Thormählen, H.-P. Seidel, Technical section: Estimating body shape of dressed humans. Comput. Graph. 33(3), 211–216 (2009)

    Google Scholar 

  92. D.C. Schneider, P. Eisert, Fast nonrigid mesh registration with a data-driven deformation prior, in NORDIA09 (2009)

    Google Scholar 

  93. R.R. Paulsen, R. Larsen, Anatomically plausible surface alignment and reconstruction, in Proceedings of Theory and Practice of, Computer Graphics (2010)

    Google Scholar 

  94. M. Wand, P. Jenke, Q.-X. Huang, M. Bokeloh, L.J. Guibas, A. Schilling, Reconstruction of deforming geometry from time-varying point clouds, in SGP (2007), pp. 49–58

    Google Scholar 

  95. A. Tevs, M. Bokeloh, M. Wand, A. Schilling, H.-P. Seidel, Isometric registration of ambiguous and partial data, in CVPR (2009)

    Google Scholar 

  96. I. Eckstein, J.-P. Pons, Y. Tong, C.-C. J. Kuo, M. Desbrun, Generalized surface flows for mesh processing, in Proceedings of the fifth Eurographics symposium on Geometry processing (2007), pp. 183–192

    Google Scholar 

  97. Q.-X. Huang, B. Adams, M. Wicke, L.J. Guibas, Non-rigid registration under isometric deformations. Comput. Graph. Forum 27(5), 1449–1457 (2008)

    Google Scholar 

  98. B. Amberg, S. Romdhani, T. Vetter, Optimal step nonrigid icp algorithms for surface registration, in CVPR (2007)

    Google Scholar 

  99. R. Sagawa, K. Akasaka, Y. Yagi, H. Hamer, L. van Gool, Elastic convolved icp for the registration of deformable objects, in Proceedings of IEEE 12th International Conference on Computer Vision Workshops (3DIM2009) (2009), pp. 1558–1565

    Google Scholar 

  100. H. Abdelmunim, A.A. Farag, Elastic shape registration using an incremental free form deformation approach with the icp algorithm, in Canadian Conference on Computer and Robot Vision (CRV) (2011), pp. 212–218

    Google Scholar 

  101. T. Windheuser, U. Schlickewei, Frank R. Schmidt, D.Cremers, Large-scale integer linear programming for orientation-preserving 3d shape matching, in Computer Graphics Forum (Proceedings Symposium Geometry Processing) (2011)

    Google Scholar 

  102. D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, J. Davis, Scape: shape completion and animation of people. ACM Trans. Graph. 24(3), 408–416 (2005)

    Google Scholar 

  103. B. Allen, B. Curless, Z. Popović, The space of human body shapes: reconstruction and parameterization from range scans, in ACM SIGGRAPH (2003), pp. 587–594

    Google Scholar 

  104. B. Allen, B. Curless, Z. Popović, A. Hertzmann, Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis, in Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer, animation (2006), pp. 147–156

    Google Scholar 

  105. H. Chen, B. Bhanu, Global-to-local non-rigid shape registration, in ICPR ’06 (2006), pp. 57–60

    Google Scholar 

  106. D. Hähnel, S. Thrun, W. Burgard,. An extension of the icp algorithm for modeling nonrigid objects with mobile robots, in Proceedings of 18th International Joint Conference on, Artificial Intelligence (IJCAI-03) (2003), pp. 915–920

    Google Scholar 

  107. N. Hasler, C. Stoll, M. Sunkel, B. Rosenhahn, H.-P. Seidel. A statistical model of human pose and body shape. in Proceedings of Eurographics 2008 Computer Graphics Forum (2009)

    Google Scholar 

  108. B. Brown, S. Rusinkiewicz, Global non-rigid alignment of 3d scans. ACM Trans. Graph. (Proc. SIGGRAPH) 26(3), 1–8 (2007)

    Google Scholar 

  109. N. Thorstensen, R. Keriven, Non-rigid shape matching using geometry and photometry. in ACCV (2009), pp. 644–654

    Google Scholar 

  110. D. Münch, B. ît Combès, S. Prima, A modified icp algorithm for normal-guided surface registration. Proc. SPIE 7623 (2010)

    Google Scholar 

  111. L.-P. Morency, T. Darrell, Stereo tracking using icp and normal flow constraint, in Proceedings of 16th International Conference on Pattern Recognition, vol. 4 (2002)

    Google Scholar 

  112. Z.-Q Cheng, W. Jiang, G. Dang, R.R. Martin, J. Li, H. Li, Y. Chen, Y. Wang, B. Li, K. Xu, S. Jin, Non-rigid registration in 3d implicit vector space, in SMI ’10 (2010). pp. 37–46

    Google Scholar 

  113. C. Papazov, D. Burschka, Deformable 3d shape registration based on local similarity transforms. Comput. Graph. Forum 30(5), 1493–1502 (2011)

    Google Scholar 

  114. T. Popa, I. South-Dickinson, D. Bradley, A. Sheffer, W. Heidrich. Globally consistent space-time reconstruction. Comput. Graph. Forum (Proc. SGP) 7729, 133–147 (2010)

    Google Scholar 

  115. H. Li, M. Pauly, First steps toward the automatic registration of deformable scans. (Technical report, ETH Zurich, 2007)

    Google Scholar 

  116. A. Sharf, D.A. Alcantara, T. Lewiner, C. Greif, A. Sheffer, N. Amenta, D. Cohen-Or, Space-time surface reconstruction using incompressible flow. ACM Trans. Graph. (2008)

    Google Scholar 

  117. N.J. Mitra, S. Flory, M. Ovsjanikov, N. Gelfand, L. Guibas, H. Pottmann, Dynamic geometry registration, in Symposium on Geometry Processing (2007), pp. 173–182

    Google Scholar 

  118. M. Liao, Q. Zhang, H. Wang, R. Yang, M. Gong, Modeling deformable objects from a single depth camera, in ICCV (2009), pp. 167–174

    Google Scholar 

  119. Z.-Q. Cheng, H. Li, J. Li, Y. Chen, Y. Wang, B. Li, X. Kai, G. Dang, S. Jin, Robust non-rigid registration of large-difference deformed modelss, in CASA (2010)

    Google Scholar 

  120. H. Li, R.W. Sumner, M. Pauly, Global correspondence optimization for non-rigid registration of depth scans. Comput. Graph. Forum (Proc. SGP’08) 25(5), 1459–1468 (2008)

    Google Scholar 

  121. H. Li, B. Adams, L.J. Guibas, M. Pauly, Robust single-view geometry and motion reconstruction. ACM Trans. Graph. (Proceedings SIGGRAPH, Asia 2009) 28(5), 175–185 (2009)

    Google Scholar 

  122. A. Tevs, A. Berner, M. Wand, I. Ihrke, M. Bokeloh, J. Kerber, H.-P. Seidel, Animation cartography—intrinsic reconstruction of shape and motion. ACM Trans. Graph. 31, 12–27 (2012)

    Google Scholar 

  123. C. Budd, A, Hilton, Temporal alignment of 3d video sequences using shape and appearance, in CVMP (2010), pp. 114–122

    Google Scholar 

  124. M. Klaudiny, A. Hilton, Cooperative patch-based 3d surface tracking, in CVMP (2011), pp. 67–76

    Google Scholar 

  125. P. Huang, C. Budd, A. Hilton, Global temporal registration of multiple non-rigid surface sequences, in CVPR (2011), pp. 3473–3480

    Google Scholar 

  126. C. Budd, P. Huang, A. Hilton, Hierarchical shape matching for temporally consistent 3d video, in |textit3DIMPVT (2011), pp. 172–179

    Google Scholar 

  127. H. Li, L. Luo, D. Vlasic, P. Peers, J. Popović, M. Pauly, S. Rusinkiewicz, Temporally coherent completion of dynamic shapes. ACM Trans. Graph. 31(1), 11 (2012)

    Google Scholar 

  128. J. Süssmuth, M. Zollhöfer, G. Greiner, Animation transplantation. J. Vis. Comput. Animation, 21, 173–182 (2010)

    Google Scholar 

  129. J. Tong, J. Zhou, L. Liu, Z. Pan, H. Yan, Scanning 3d full human bodies using kinects. IEEE Trans. Vis. Comput. Graph. (Proceedings of IEEE Virtual Reality) 18(4), 643–650 (2012)

    Google Scholar 

  130. M. Desbrun, M. Meyer, P. Schröder, A.H. Barr, Implicit fairing of irregular meshes using diffusion and curvature flow, in SIGGRAPH (1999), pp. 317–324

    Google Scholar 

  131. M. Botsch, O. Sorkine, On linear variational surface deformation methods. IEEE Trans. Vis. Comput. Graph. 14(1), 213–230 Jan (2008)

    Google Scholar 

  132. I. Wald, S. Boulos, P. Shirley, Ray tracing deformable scenes using dynamic bounding volume hierarchies. ACM Trans. Graph. 26(1), 18 (2007)

    Google Scholar 

  133. I. Wald, On fast construction of sah-based bounding volume hierarchies, in Proceedings of the 2007 IEEE Symposium on Interactive Ray Tracing, RT ’07 (2007), pp. 33–40

    Google Scholar 

  134. A. Jacobson, I. Baran, J. Popović, O. Sorkine, Bounded biharmonic weights for real-time deformation. ACM Trans. Graph. 30, 165:1–165:8 (2011)

    Google Scholar 

  135. O.K.-C. Au, H. Fu, C.-L. Tai, D. Cohen-Or, Handle-aware isolines for scalable shape editing. ACM Trans. Graph. 26, 83 (2007)

    Google Scholar 

  136. D. Vlasic, I. Baran, W. Matusik, J. Popović, Articulated mesh animation from multi-view silhouettes. ACM Trans. Graph. 27(3), 1–97 Aug (2008)

    Google Scholar 

  137. J. Shi, C. Tomasi. Good features to track, in CVPR’94 (1994), pp. 593–600

    Google Scholar 

  138. B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in IJCAI’81 (1981), pp. 674–679

    Google Scholar 

  139. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in ECCV (4)’04 (2004), pp. 25–36

    Google Scholar 

  140. D.G. Lowe, Object recognition from local scale-invariant features, in ICCV ’99 (1999)

    Google Scholar 

  141. H. Bay, A. Ess, T. Tuytelaars, L. van Gool, Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)

    Google Scholar 

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Correspondence to Yann Savoye .

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Savoye, Y. (2014). Toward Non-rigid Dynamic Cage Capture. In: Cage-based Performance Capture. Studies in Computational Intelligence, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-319-01538-5_4

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