Skip to main content
SpringerLink
Log in
Book cover

3D Imaging, Analysis and Applications pp 221–264Cite as

3D Shape Registration

  • Chapter

Abstract

Registration is the problem of bringing together two or more 3D shapes, either of the same object or of two different but similar objects. This chapter first introduces the classical Iterative Closest Point (ICP) algorithm, which represents the gold standard registration method. Current limitations of ICP are addressed and the most popular variants are described to improve the basic implementation in several ways. Challenging registration scenarios are analyzed and a taxonomy of recent and promising alternative registration techniques is introduced. Three case studies are then described with an increasing level of problem difficulty. The first case study describes a simple but effective technique to detect outliers. The second case study uses the Levenberg-Marquardt optimization procedure to solve standard pairwise registration. The third case study focuses on the challenging problem of deformable object registration. Finally, open issues and directions for future work are discussed and conclusions are drawn.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.95
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Note that the pair (d i ,m j ) is initially a tentative correspondence, which becomes a true correspondence when convergence to a global minimum is attained.

  2. 2.

    Piazza Brà, Verona, Italy. Image courtesy of Gexcel: http://www.gexcel.it.

  3. 3.

    Experimental material is based on the survey paper [79]. Objects and code are available at http://eia.udg.es/cmatabos/research.htm.

  4. 4.

    In order to visualize the peak the second part of the histogram has been quantized with wider intervals.

  5. 5.

    Note that the volume is discretized into integer values, therefore the data-point d i should be rounded to recover X(d i ).

  6. 6.

    A multiplication between two quaternions q and \(\mathbf{q'}\) is defined as \([s s' - \mathbf{v} \cdot\mathbf{v'}, \mathbf{v} \times\mathbf{v'} + s \mathbf {v'} + s' \mathbf{v}]\).

  7. 7.

    While we have chosen the identity as the damping matrix, some authors rather choose the diagonal part of the Gauss-Newton Hessian approximation.

  8. 8.

    http://research.microsoft.com/en-us/um/people/awf/lmicp.

  9. 9.

    The object boundaries can be estimated according to the kind of sensor being used. For instance boundaries on range scans can be estimated on the range image. In stereo sensors, they can be estimated on one of the two optical views.

  10. 10.

    Recall that the model points lie on a grid.

  11. 11.

    The damped Gauss-Newton approximation to the true Hessian matrix.

  12. 12.

    Data courtesy of eVS (http://www.evsys.net).

  13. 13.

    Matlab implementation at: http://www.csse.uwa.edu.au/ajmal/code.html.

  14. 14.

    Matlab implementation at: http://research.microsoft.com/en-us/um/people/awf/lmicp.

References

  1. Albarelli, A., Torsello, A., Rodola, E.: A game-theoretic approach to fine surface registration without initial motion estimation. In: International Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  2. Anguelov, D., Srinivasan, P., Pang, H.C., Koller, D., Thurun, S., Davis, J.: The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces. In: Neural Information Processing Systems Conference (2004)

    Google Scholar 

  3. Arun, K.S., Huang, T., Blostein, S.: Least-squares fitting of two 3-d point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987)

    Article  Google Scholar 

  4. Bariya, P., Nishino, K.: Scale-hierarchical 3d object recognition in cluttered scenes. In: International Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  5. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  6. Bernardini, F., Rushmeier, H.: The 3D model acquisition pipeline. Comput. Graph. Forum 21(2), 149–172 (2002)

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Blais, G., Levine, M.: Registering multiview range data to create 3d computer objects. IEEE Trans. Pattern Anal. Mach. Intell. 17(8) (1995)

    Google Scholar 

  9. Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches and challenges in 3d and multi-modal 3d + 2d face recognition. Comput. Vis. Image Underst. 101(1) (2006)

    Google Scholar 

  10. Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three-dimensional face recognition. Int. J. Comput. Vis. 64(1), 5–30 (2005)

    Article  Google Scholar 

  11. Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Numerical Geometry of Non-rigid Shapes. Springer, Berlin (2008)

    MATH  Google Scholar 

  12. Brown, B., Rusinkiewicz, S.: Non-rigid range-scan alignment using thin-plate splines. In: Symposium on 3D Data Processing, Visualization, and Transmission (2004)

    Google Scholar 

  13. Brown, B., Rusinkiewicz, S.: Global non-rigid alignment of 3-D scans. ACM Trans. Graph. 26(3) (2007) (Proc. SIGGRAPH)

    Google Scholar 

  14. Brusco, N., Andreetto, M., Giorgi, A., Cortelazzo, G.: 3d registration by textured spin-images. In: 3DIM’05: Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling, pp. 262–269 (2005)

    Chapter  Google Scholar 

  15. Campbell, R., Flynn, P.: A survey of free-form object representation and recognition techniques. Comput. Vis. Image Underst. 81(2), 166–210 (2001)

    Article  MATH  Google Scholar 

  16. Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. In: Computer Graphics Forum, vol. 27, pp. 643–652. Blackwell, Oxford (2008)

    Google Scholar 

  17. Castellani, U., Fusiello, A., Murino, V.: Registration of multiple acoustic range views for underwater scene reconstruction. Comput. Vis. Image Underst. 87(3), 78–89 (2002)

    Article  Google Scholar 

  18. Castellani, U., Fusiello, A., Murino, V., Papaleo, L., Puppo, E., Pittore, M.: A complete system for on-line modelling of acoustic images. Image Commun. J. 20(9–10), 832–852 (2005)

    Google Scholar 

  19. Castellani, U., Gay-Bellile, V., Bartoli, A.: Robust deformation capture from temporal range data for surface rendering. Comput. Animat. Virtual Worlds 19(5), 591–603 (2008)

    Article  Google Scholar 

  20. Chang, M., Leymarie, F., Kimia, B.: 3d shape registration using regularized medial scaffolds. In: International Symposium on 3D Data Processing, Visualization and Transmission (2004)

    Google Scholar 

  21. Chang, W., Zwicker, M.: Automatic registration for articulated shapes. Comput. Graph. Forum 27(5), 1459–1468 (2008) (Proceedings of SGP 2008)

    Article  Google Scholar 

  22. Chang, W., Zwicker, M.: Range scan registration using reduced deformable models. Comput. Graph. Forum 28(2), 447–456 (2009)

    Article  Google Scholar 

  23. Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis. Comput. 10(3), 145–155 (1992)

    Article  Google Scholar 

  24. Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89(2–3), 114–141 (2003)

    Article  MATH  Google Scholar 

  25. Corey, G., Matei, C., Jaime, P.: Data-driven grasping with partial sensor data. In: IROS’09: Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1278–1283 (2009)

    Google Scholar 

  26. Cruska, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)

    Google Scholar 

  27. Cunnington, S., Stoddart, A.: N-view point set registration: a comparison. In: British Machine Vision Conference (1999)

    Google Scholar 

  28. Dewaele, G., Devernay, F., Horaud, R.: Hand motion from 3d point trajectories and a smooth surface model. In: European Conference on Computer Vision (2004)

    Google Scholar 

  29. Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3d object recognition. In: International Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  30. Eggert, D., Lorusso, A., Fisher, R.: Estimating 3-d rigid body transformations: a comparison of four major algorithms. Mach. Vis. Appl. 9, 272–290 (1997)

    Article  Google Scholar 

  31. Ezra, E., Sharir, M., Efrat, A.: On the performance of the ICP algorithm. Comput. Geom. 41(1–2), 77–93 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  32. Fitzgibbon, A.: Robust registration of 2D and 3D point sets. Image Vis. Comput. 21(13–14), 1145–1153 (2003)

    Article  Google Scholar 

  33. Funkhouser, T., Kazhdan, M., Min, P., Shilane, P.: Shape-based retrieval and analysis of 3d models. Commun. ACM 48(6), 58–64 (2005)

    Article  Google Scholar 

  34. Fusiello, A.: Visione computazionale. Appunti delle lezioni. Pubblicato a cura dell’autore (2008)

    Google Scholar 

  35. Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Desbrun, M., Pottmann, H. (eds.) EuroGraphics Association, pp. 197–206 (2005) ISBN 3-905673-24-X

    Google Scholar 

  36. Godin, G., Laurendeau, D., Bergevin, R.: A method for the registration of attributed range images. In: 3-D Digital Imaging and Modeling (3DIM), pp. 179–186 (2001)

    Google Scholar 

  37. Golovinskiy, A., Kim, V., Funkhouser, T.: Shape-based recognition of 3d point clouds in urban environments. In: International Conference on Computer Vision (2009)

    Google Scholar 

  38. Granger, S., Pennec, X.: Multi-scale em-ICP: a fast and robust approach for surface registration. In: European Conference on Computer Vision (2002)

    Google Scholar 

  39. Gu, X., Gortler, S.J., Hoppe, H.: Geometry images. ACM Trans. Graph. 21(3), 355–361 (2002)

    Article  Google Scholar 

  40. Hampel, F., Rousseeuw, P., Ronchetti, E., Stahel, W.: Robust Statistics: The Approach Based on Influence Functions. Wiley, New York (1986)

    MATH  Google Scholar 

  41. Horaud, R., Forbes, F., Yguel, M., Dewaele, G., Zhang, J.: Rigid and articulated point registration with expectation conditional maximization. IEEE Trans. Pattern Anal. Mach. Intell. 33(3), 587–602 (2011)

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Huber, D., Hebert, M.: Fully automatic registration of multiple 3D data sets. Image Vis. Comput. 21(7), 637–650 (2003)

    Article  Google Scholar 

  44. IV, A.P., Mordohai, P., Daniilidis, K.: Object detection from large-scale 3d datasets using bottom-up and top-down descriptors. In: Proceedings of the European Conference on Computer Vision (2008)

    Google Scholar 

  45. Jhonson, A., Kang, S.: Registration and integration of textured 3d data. Image Vis. Comput. 19(2), 135–147 (1999)

    Article  Google Scholar 

  46. Jian, B., Vemuri, B.C.: A robust algorithm for point set registration using mixture of Gaussians. In: International Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  47. Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)

    Article  Google Scholar 

  48. van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. In: EuroGraphics: State-of-the-Art Report (2010)

    Google Scholar 

  49. Khoualed, S., Castellani, U., Bartoli, A.: Semantic shape context for the registration of multiple partial 3-D views. In: British Machine Vision Conference (2009)

    Google Scholar 

  50. Krsek, P., Pajdla, T., Hlavác, V.: Differential invariants as the base of triangulated surface registration. Comput. Vis. Image Underst. 87(1–3), 27–38 (2002)

    Article  MATH  Google Scholar 

  51. Li, H., Sumner, R.W., Pauly, M.: Global correspondence optimization for non-rigid registration of depth scans. Comput. Graph. Forum 27(5) (2008) (Proc. SGP’08)

    Google Scholar 

  52. Liu, Y.: Automatic 3d free form shape matching using the graduated assignment algorithm. Pattern Recognit. 38, 1615–1631 (2005)

    Article  Google Scholar 

  53. Lomonosov, E., Chetverikov, D., Ekárt, A.: Pre-registration of arbitrarily oriented 3d surfaces using a genetic algorithm. Pattern Recognit. Lett. 27(11), 1201–1208 (2006)

    Article  Google Scholar 

  54. Maintz, J., Viergever, M.A.: A survey of medical image registration. Med. Image Anal. 2(1), 1–36 (1998)

    Article  Google Scholar 

  55. Makadia, A., Patterson, A., Daniilidis, K.: Fully automatic registration of 3D point clouds. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1297–1304. IEEE Computer Society, Washington (2006)

    Google Scholar 

  56. Mian, A.S., Bennamoun, M., Owens, R.: Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1584–1601 (2006)

    Article  Google Scholar 

  57. Mian, A.S., Bennamoun, M., Owens, R.A.: Automatic correspondence for 3d modeling: an extensive review. Int. J. Shape Model. 11(2), 253–291 (2005)

    Article  MATH  Google Scholar 

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

    Google Scholar 

  59. Munoz, D., Vandapel, N., Hebert, M.: Directional associative Markov network for 3-d point cloud classification. In: International Symposium on 3-D Data Processing, Visualization and Transmission (3DPVT) (2008)

    Google Scholar 

  60. Murino, V., Ronchetti, L., Castellani, U., Fusiello, A.: Reconstruction of complex environments by robust pre-aligned ICP. In: 3DIM (2001)

    Google Scholar 

  61. Myronenko, A., Song, X., Carreira-Perpinan, M.: Non-rigid point set registration: coherent point drift. In: Neural Information Processing Systems Conference (2006)

    Google Scholar 

  62. Novatnack, J., Nishino, K.: Scale-dependent/invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In: Proceedings of the 10th European Conference on Computer Vision: Part III, pp. 440–453. Springer, Berlin (2008)

    Google Scholar 

  63. Nuchter, A., Lingemann, K., Hertzberg, J.: Cached k-d tree search for ICP algorithms. In: 3DIM’07: Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling, pp. 419–426 (2007)

    Chapter  Google Scholar 

  64. Park, K., Germann, M., Breitenstein, M.D., Pfister, H.: Fast and automatic object pose estimation for range images on the GPU. Mach. Vis. Appl. 21(5), 749–766 (2009)

    Article  Google Scholar 

  65. Park, S., Subbarao, M.: An accurate and fast point-to-plane registration technique. Pattern Recognit. Lett. 24(16), 2967–2976 (2003)

    Article  Google Scholar 

  66. Pears, N.E., Heseltine, T., Romero, M.: From 3d point clouds to pose normalised depth maps. Int. J. Comput. Vis. 89(2), 152–176 (2010)

    Article  Google Scholar 

  67. Phillips, J., Liu, R., Tomasi, C.: Outlier robust ICP for minimizing fractional RMSD. In: 3-D Digital Imaging and Modeling (3DIM), pp. 427–434 (2007)

    Google Scholar 

  68. Pissanetzky, S.: Sparse Matrix Technology. Academic Press, San Diego (1984)

    MATH  Google Scholar 

  69. Pottmann, H., Huang, Q., Yang, Y., Hu, S.: Geometry and convergence analysis of algorithms for registration of 3D shapes. Int. J. Comput. Vis. 67(3), 277–296 (2006)

    Article  Google Scholar 

  70. Prasad, M., Zisserman, A., Fitzgibbon, A.W.: Single view reconstruction of curved surfaces. In: International Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  71. Pulli, K.: Multiview registration for large data sets. In: 3DIM’99: Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling, pp. 160–168 (1999)

    Google Scholar 

  72. Pulli, K., Piiroinen, S., Duchamp, T., Stuetzle, W.: Projective surface matching of colored 3d scans. In: 3-D Digital Imaging and Modeling (3DIM), pp. 531–538 (2005)

    Google Scholar 

  73. Rangarajan, A., Chui, H., Duncan, J.: Rigid point feature registration using mutual information. Med. Image Anal. 3(4), 425–440 (1999)

    Article  Google Scholar 

  74. Rangarajan, A., Chui, H., Mjolsness, E., Pappu, S., Davachi, L., Goldman-Rakic, P., Duncan, J.: A robust point-matching algorithm for autoradiograph alignment. Med. Image Anal. 1(4), 379–398 (1997)

    Article  Google Scholar 

  75. Ruiter, H.D., Benhabib, B.: On-line Modeling for Real-Time, Model-Based, 3D Pose Tracking. Springer, Berlin (2007)

    Google Scholar 

  76. Rusinkiewicz, S., Brown, B., Kazhdan, M.: 3d Scan Matching and Registration. ICCV Short Course (2005)

    Google Scholar 

  77. Rusinkiewicz, S., Hall-Holt, O., Levoy, M.: Real-time 3-D model acquisition. ACM Trans. Graph. 21(3), 438–446 (2002) (Proc. SIGGRAPH)

    Article  Google Scholar 

  78. Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Third International Conference on 3-D Digital Imaging and Modeling, 2001, Proceedings, pp. 145–152 (2001)

    Chapter  Google Scholar 

  79. Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vis. Comput. 25(5), 578–596 (2007)

    Article  Google Scholar 

  80. Salzmann, M., Ilic, S., Fua, P.: Physically valid shape parameterization for monocular 3-D deformable surface tracking. In: British Machine Vision Conference (2005)

    Google Scholar 

  81. Sara, R.: Finding the largest unambiguous component of stereo matching. In: Proc. of European Conference on Computer Vision (ECCV), pp. 900–914 (2002)

    Google Scholar 

  82. Sara, R., Okatani, I., Sugimoto, A.: Globally convergent range image registration by graph kernel algorithm. In: 3-D Digital Imaging and Modeling (3DIM) (2005)

    Google Scholar 

  83. Scheenstra, A., Ruifrok, A., Veltkamp, R.C.: A survey of 3d face recognition methods. In: Audio- and Video-Based Biometric Person Authentication, pp. 891–899 (2005)

    Chapter  Google Scholar 

  84. Shams, R., Sadeghi, P., Kennedy, R.A., Hartley, R.I.: A survey of high performance medical image registration on multi-core, GPU and distributed architectures. IEEE Signal Process. Mag. 27(2), 50–60 (2010)

    Article  Google Scholar 

  85. Sharp, G., Sang, L., Wehe, D.: ICP registration using invariant features. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 90–102 (2002)

    Article  Google Scholar 

  86. Silva, L., Bellon, O.R.P., Boyer, K.L.: Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 762–776 (2005)

    Article  Google Scholar 

  87. Simon, D.A.: Fast and accurate shape-based registration. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA, USA (1996)

    Google Scholar 

  88. Steinke, F., Scholkopf, B., Blanz, V.: Learning dense 3d correspondence. In: Annual Conference on Neural Information Processing Systems (NIPS 2006) (2007)

    Google Scholar 

  89. Taati, B., Bondy, M., Jasiobedzki, P., Greenspan, M.: Automatic registration for model building using variable dimensional local shape descriptors. In: International Conference on 3-D Digital Imaging and Modeling (2007)

    Google Scholar 

  90. Tangelder, J., Veltkamp, R.: A survey of content based 3d shape retrieval methods. Multimed. Tools Appl. 39(3), 441–471 (2008)

    Article  Google Scholar 

  91. Toldo, R., Beinat, A., Crosilla, F.: Global registration of multiple point clouds embedding the generalized procrustes analysis into an ICP framework. In: Symposium on 3D Data Processing, Visualization, and Transmission (2010)

    Google Scholar 

  92. Trucco, M., Verri, A.: Introductory Techniques for 3-D Computer Vision. Prentice Hall, New York (1998)

    Google Scholar 

  93. Tsin, Y., Kanade, T.: A correlation-based approach to robust point set registration. In: European Conference on Computer Vision, pp. 558–569 (2004)

    Google Scholar 

  94. Umeyama, S.: Least-squares estimation of transformation parameters between two points patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 376–380 (1991)

    Article  Google Scholar 

  95. Vinesh, R., Kiran, F.: Reverse Engineering, an Industrial Perspective. Springer, Berlin (2008)

    Google Scholar 

  96. Wang, F., Vemuri, B.C., Rangarajan, A.: Groupwise point pattern registration using a novel CDF-based Jensen-Shannon divergence. In: International Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  97. Watt, A.: 3D Computer Graphics. Addison-Wesley, Reading (2000)

    Google Scholar 

  98. Weik, S.: Registration of 3-d partial surface models using luminance and depth information. In: 3-D Digital Imaging and Modeling (3DIM), pp. 93–100 (1997)

    Google Scholar 

  99. Wyngaerd, J.V., Gool, L.V.: Automatic crude patch registration: toward automatic 3d model building. Comput. Vis. Image Underst. 87(1–3), 8–26 (2002)

    Article  MATH  Google Scholar 

  100. Zhang, Z.: Iterative point matching of free-form curves and surfaces. Int. J. Comput. Vis. 13(2), 119–152 (1994)

    Article  Google Scholar 

  101. Zinsser, T., Schnidt, H., Niermann, J.: A refined ICP algorithm for robust 3D correspondences estimation. In: International Conference on Image Processing, pp. 695–698 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Verona, Verona, Italy

    Umberto Castellani

  2. Université d’Auvergne, Clermont-Ferrand, France

    Adrien Bartoli

Authors
  1. Umberto Castellani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adrien Bartoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Umberto Castellani .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Deramore Lane, Heslington, York, YO10 5GH, United Kingdom

    Nick Pears

  2. Department of Computer Science, Aberystwyth University, Llandinam Building, Ceredigion, Aberystwyth, SY23 3DB, United Kingdom

    Yonghuai Liu

  3. Institute of Geography and Earth Science, Aberystwyth University, Penglais Campus, Ceredigion, Aberystwyth, SY23 3DB, United Kingdom

    Peter Bunting

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London

About this chapter

Cite this chapter

Castellani, U., Bartoli, A. (2012). 3D Shape Registration. In: Pears, N., Liu, Y., Bunting, P. (eds) 3D Imaging, Analysis and Applications. Springer, London. https://doi.org/10.1007/978-1-4471-4063-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4063-4_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4062-7

  • Online ISBN: 978-1-4471-4063-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation