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Journal of Real-Time Image Processing

, Volume 15, Issue 2, pp 329–347 | Cite as

Efficient registration of 2D points to CAD models for real-time applications

  • Rubén Usamentiaga
  • Daniel F. García
  • Julio Molleda
Original Research Paper

Abstract

Efficient registration is a major challenge for real-time machine vision applications. Modern acquisition hardware can produce data at extremely high rates. Thus, efficient registration algorithms are required to align data to reference models to detect deviations and take correcting actions if needed. In this work, an efficient registration procedure of 2D points to CAD (computer-aided design) models is proposed. Recent developments in the field are reviewed and evaluated in terms of accuracy, speed and robustness. Efficient algorithms are proposed for the most computationally expensive parts of the registration, including an estimation of the rigid transform, a calculation of the closest point to geometric primitives, and an estimation of the surface normal. Furthermore, a novel primitive caching procedure is proposed that, when combined with an R-tree, greatly improves the execution speed of the registration. The result is a very accurate registration procedure, since geometric primitives are treated analytically with no point sampling required. At the same time, the proposed procedure is robust, very fast, and can achieve the correct registration in less than one millisecond.

Keywords

Point cloud registration CAD model registration ICP R-trees Primitives caching 

Notes

Acknowledgments

This work has been partially funded by the project TIN2001-24903 of the Spanish National Plan for Research, Development and Innovation.

References

  1. 1.
    Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. Symposium on geometry processing 2(3), 5 (2005)Google Scholar
  2. 2.
    Varady, T., Martin, R.R., Cox, J.: Reverse engineering of geometric modelsan introduction. Comput Aided Design 29(4), 255–268 (1997)CrossRefGoogle Scholar
  3. 3.
    Raja, V., Fernandes, K.J.: Reverse engineering: an industrial perspective. Springer (2007)Google Scholar
  4. 4.
    Pieraccini, M., Guidi, G., Atzeni, C.: 3d digitizing of cultural heritage. J Cult Heritage 2(1), 63–70 (2001)CrossRefGoogle Scholar
  5. 5.
    Vlahakis, V., Ioannidis, N., Karigiannis, J., Tsotros, M., Gounaris, M., Stricker, D., Gleue, T., Daehne, P., Almeida, L.: Archeoguide: an augmented reality guide for archaeological sites. IEEE Comput Graphics Appl 22(5), 52–60 (2002)CrossRefGoogle Scholar
  6. 6.
    Wang, C.-C., Thorpe, C., Thrun, S., Hebert, M., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. Int J Robot Res 26(9), 889–916 (2007)CrossRefGoogle Scholar
  7. 7.
    Goldfeder, C., Ciocarlie, M., Peretzman, J., Dang, H., Allen, P.K.: Data-driven grasping with partial sensor data. in Intelligent Robots and Systems: IROS 2009. IEEE/RSJ International Conference on. IEEE 2009, 1278–1283 (2009)Google Scholar
  8. 8.
    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 Understand 101(1), 1–15 (2006)CrossRefGoogle Scholar
  9. 9.
    Castellani, U., Bartoli, A.: “3d shape registration,” in 3D Imaging, Analysis and Applications. Springer, pp. 221–264 (2012)Google Scholar
  10. 10.
    Salvi, J., Pages, J., Batlle, J.: Pattern codification strategies in structured light systems. Pattern Recogn 37(4), 827–849 (2004)CrossRefMATHGoogle Scholar
  11. 11.
    Studholme, C., Hill, D.L., Hawkes, D.J.: Automated 3-d registration of mr and ct images of the head. Medical Image Anal 1(2), 163–175 (1996)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2), 239–256 (1992)CrossRefGoogle Scholar
  14. 14.
    Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vis Comput 10(3), 145–155 (1992)CrossRefGoogle Scholar
  15. 15.
    Li, H., Sumner, R.W., Pauly, M.: “Global correspondence optimization for non-rigid registration of depth scans,” in Computer Graphics Forum, vol. 27, no. 5., pp. 1421–1430. Wiley Online Library (2008)Google Scholar
  16. 16.
    Pulli, K.: Multiview registration for large data sets. in 3-D Digital Imaging and Modeling. In: Proceedings. Second International Conference on. IEEE 1999, 160–168 (1999)Google Scholar
  17. 17.
    Jost, T., Hugli, H.: A multi-resolution icp with heuristic closest point search for fast and robust 3d registration of range images. In: 3-D Digital Imaging and Modeling: 3DIM 2003. Proceedings. Fourth International Conference on. IEEE 2003, 427–433 (2003)Google Scholar
  18. 18.
    Fitzgibbon, A.W.: Robust registration of 2d and 3d point sets. Image Vis Comput 21(13), 1145–1153 (2003)CrossRefGoogle Scholar
  19. 19.
    Rangarajan, A., Chui, H., Mjolsness, E., Pappu, S., Davachi, L., Goldman-Rakic, P., Duncan, J.: A robust point-matching algorithm for autoradiograph alignment. Medical Image Anal 1(4), 379–398 (1997)CrossRefGoogle Scholar
  20. 20.
    Sharp, G.C., Lee, S.W., Wehe, D.K.: Icp registration using invariant features. Pattern Anal Mach Intell IEEE Trans 24(1), 90–102 (2002)CrossRefGoogle Scholar
  21. 21.
    Park, S.-Y., Subbarao, M.: An accurate and fast point-to-plane registration technique. Pattern Recogn Lett 24(16), 2967–2976 (2003)CrossRefGoogle Scholar
  22. 22.
    Rusinkiewicz, S., Levoy, M.: “Efficient variants of the icp algorithm,” in 3-D Digital Imaging and Modeling. Proceedings 3rd International Conference on IEEE, 145–152 (2001)Google Scholar
  23. 23.
    Nuchter, A., Lingemann, K., Hertzberg, J.: Cached kd tree search for icp algorithms. In: 3-D Digital Imaging and Modeling: 3DIM’07. Sixth International Conference on. IEEE 2007, 419–426 (2007)Google Scholar
  24. 24.
    Park, I.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 (2010)CrossRefGoogle Scholar
  25. 25.
    Esteghamatian, M., Azimifar, Z., Radau, P., Wright, G.: Real time cardiac image registration during respiration: a time series prediction approach. J Real Time Image Process 8(2), 179–191 (2013)CrossRefGoogle Scholar
  26. 26.
    Usamentiaga, R., Molleda, J., García, D.F.: Fast and robust laser stripe extraction for 3d reconstruction in industrial environments. Mach Vis Appl 23(1), 179–196 (2012)CrossRefGoogle Scholar
  27. 27.
    Kehtarnavaz, N., Gamadia, M.: Real-time image and video processing: from research to reality. Synthesis Lectures on Image, Video & Multimedia Processing 2(1), 1–108 (2006)Google Scholar
  28. 28.
    Arun, K., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. Patt. Anal. Machine. Intell. IEEE. Trans. 5, 698–700 (1987)Google Scholar
  29. 29.
    Elseberg, J., Magnenat, S., Siegwart, R., Nüchter, A.: Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration. J Software Eng Robot 3(1), 2–12 (2012)Google Scholar
  30. 30.
    Bentley, J.L.: “K-d trees for semidynamic point sets,” in Proceedings of the sixth annual symposium on Computational geometry. ACM, pp. 187–197 (1990)Google Scholar
  31. 31.
    Jia, Y., Wang, J., Zeng, G., Zha, H., Hua, X.-S.:Optimizing kd-trees for scalable visual descriptor indexing in Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 3392–3399 (2010)Google Scholar
  32. 32.
    Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. Patt. Anal. Machine. Intell. IEEE. Trans. 36, (2014)Google Scholar
  33. 33.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, (2003)Google Scholar
  34. 34.
    Eggert, D.W., Lorusso, A., Fisher, R.B.: Estimating 3-d rigid body transformations: a comparison of four major algorithms. Mach Vis Appl 9(5–6), 272–290 (1997)CrossRefGoogle Scholar
  35. 35.
    Gower, J.C., Dijksterhuis, G.B.: Procrustes problems. Oxford University Press, Oxford, vol. 3 (2004)Google Scholar
  36. 36.
    Challis, J.H.: A procedure for determining rigid body transformation parameters. J Biomech 28(6), 733–737 (1995)CrossRefGoogle Scholar
  37. 37.
    Schneider, P., Eberly, D.H.: Geometric tools for computer graphics. Morgan Kaufmann (2002)Google Scholar
  38. 38.
    Hill, J.F.: “The pleasures of perp dot products,” in Graphics gems IV. Academic Press, San Diego (1994)Google Scholar
  39. 39.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. ACM, vol. 14, no. 2 (1984)Google Scholar
  40. 40.
    Simon, D.: “Fast and accurate shape-based registration,” Ph.D. dissertation, Robotics Institute, Carnegie Mellon University, Pittsburgh (1996)Google Scholar
  41. 41.
    Klasing, K., Althoff, D., Wollherr, D., Buss, M.: Comparison of surface normal estimation methods for range sensing applications. in Robotics and Automation, : ICRA’09. IEEE International Conference on. IEEE 2009, 3206–3211 (2009)Google Scholar
  42. 42.
    Woo, H., Kang, E., Wang, S., Lee, K.H.: A new segmentation method for point cloud data. Int J Mach Tools Manufact 42(2), 167–178 (2002)CrossRefGoogle Scholar
  43. 43.
    Yau, H.-T., Kuo, C.-C., Yeh, C.-H.: Extension of surface reconstruction algorithm to the global stitching and repairing of stl models. Comput Aided Design 35(5), 477–486 (2003)CrossRefGoogle Scholar
  44. 44.
    Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T.: Computing and rendering point set surfaces. Visualization and Computer Graphics, IEEE Transactions on 9(1), 3–15 (2003)Google Scholar
  45. 45.
    OuYang, D., Feng, H.-Y.: On the normal vector estimation for point cloud data from smooth surfaces. Comput Aided Design 37(10), 1071–1079 (2005)CrossRefGoogle Scholar
  46. 46.
    Mitra, N.J., Nguyen, A., Guibas, L.: Estimating surface normals in noisy point cloud data. Int J Comput Geometry Appl 14(04n05), 261–276 (2004)MathSciNetCrossRefMATHGoogle Scholar
  47. 47.
    Berkmann, J., Caelli, T.: Computation of surface geometry and segmentation using covariance techniques. Pattern Analysis and Machine Intelligence, IEEE Transactions on 16(11), 1114–1116 (1994)Google Scholar
  48. 48.
    Phillips, J.M., Liu, R., Tomasi, C.: Outlier robust icp for minimizing fractional rmsd. in 3-D Digital Imaging and Modeling, : 3DIM’07. Sixth International Conference on. IEEE 2007, 427–434 (2007)Google Scholar
  49. 49.
    Castellani, U., Fusiello, A., Murino, V.: Registration of multiple acoustic range views for underwater scene reconstruction. Comput Vis Image Understand 87(1), 78–89 (2002)CrossRefMATHGoogle Scholar
  50. 50.
    Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust statistics: the approach based on influence functions. John Wiley & Sons, vol. 114 (2011)Google Scholar
  51. 51.
    Masuda, T., Sakaue, K., Yokoya, N.: “Registration and integration of multiple range images for 3-d model construction,” in Pattern Recognition. In: Proceedings of the 13th International Conference on, vol. 1. IEEE, pp. 879–883 (1996)Google Scholar
  52. 52.
    Turk, G., Levoy, M.: “Zippered polygon meshes from range images,” in Proceedings of the 21st annual conference on Computer graphics and interactive techniques. ACM, pp. 311–318 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Rubén Usamentiaga
    • 1
  • Daniel F. García
    • 1
  • Julio Molleda
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of OviedoAsturiasSpain

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