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An efficient algorithm for non-rigid object registration

A. Makovetskii 1, S. Voronin 1, V. Kober 1, A. Voronin 1

Chelyabinsk State University, ul. Bratiev Kashirinykh, 129, 454001, Chelyabinsk, Russia

 PDF, 509 kB

DOI: 10.18287/2412-6179-CO-586

Страницы: 67-73.

Язык статьи: English.

An efficient algorithm for registration of two non-rigid objects based on geometrical transformation of the template object to target object is proposed. The transformation is considered as warping of the template onto the target. To choose the most suitable transformation from all possible warps, a registration algorithm should satisfy deformation constraints referred to as regularization of non-rigid objects. In this work, we use variational functionals for affine transformations. With the help of computer simulation, the proposed method for searching the optimal geometrical transformation is compared with that of common algorithms.

Ключевые слова:
iterative closest points, nonrigid ICP, shape registration, affine transformation, orthogonal transformation, point-to-point, point-to-plane, deformable surfaces.

Makovetskii, A. An efficient algorithm for non-rigid object registration / A. Makovetskii, S. Voronin, V. Kober, A. Voronin // Компьютерная оптика. – 2020. – Т. 44, № 1. – С. 67-73. – DOI: 10.18287/2412-6179-CO-586.


  1. Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Patt Anal Machine Intell 1992; 14(2): 239-256.
  2. Chen Y, Medioni G. Object modeling by registration of multiple range images. Proc IEEE Conf Robot Automat 1991; 3: 2724-2729.
  3. Amberg B, Romdhani S, Vetter T. Optimal step nonrigid icp algorithms for surface registration. Proc IEEE Conf Comput Vis Patt Recogn 2007: 1-8.
  4. Cheng S, Marras I, Zafeiriou S, Pantic M. Active nonrigid ICP algorithm. IEEE International Conference and Workshops on Automatic Face and Gesture Recognition 2015: 1-8.
  5. Blanz V, Vetter T. A morphable model for the synthesis of 3D faces. Proc SIGGRAPH 1999: 187-194.
  6. Davis J, Marschner S, Garr M, Levoy M. Filling holes in complex surfaces using volumetric diffusion. Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission 2002: 428-441.
  7. Kahler K, Haber J, Yamauchi H, Seidel H-P. Head shop: Generating animated head models with anatomical structure. Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2002: 55-63.
  8. Szeliski R, Lavalle S. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines. Int J Comp Vis 1996; 18(2): 171-186.
  9. Allen B, Curless B, Popovic Z. The space of human body shapes: Reconstruction and parameterization from range scans. ACM Trans on Graph 2003; 22(3): 587-594.
  10. Fischer B, Modersitzki J. Curvature based image registration. J Math Imaging Vis 2003; 18(1): 81-85.
  11. Dekker M, Feldmar J, Ayache N. Rigid, affine and locally affine registration of free-form surfaces. Int J Comp Vis 1996; 18(2): 99-119.
  12. Picos K, Diaz-Ramirez V, Kober V, Montemayor A, Pantrigo J. Accurate three-dimensional pose recognition from monocular images using template matched filtering. Opt Eng 2016; 55(6): 063102.
  13. Echeagaray-Patron B, Kober V, Karnaukhov V, Kuznetsov V. A method of face recognition using 3D facial surfaces. J Commun Technol Electron 2017; 62: 648-652.
  14. Echeagaray-Patron B, Miramontes-Jaramillo D, Kober V. Conformal parameterization and curvature analysis for 3D facial recognition. Proc IEEE Int Conf Comput Sci Comput Intelligence 2015: 843-844.
  15. Ruchay A, Dorofeev K, Kolpakov V. Fusion of information from multiple kinect sensors for 3D object reconstruction. Computer Optics 2018; 42(5): 898-903.
  16. Ruchay A, Dorofeev K, Kober A. 3D object reconstruction using multiple Kinect sensors and initial estimation of sensor parameters. Proc SPIE 2018; 10752: 1075222.
  17. Ruchay A, Dorofeev K, Kober A. Accurate reconstruction of the 3D indoor environment map with a RGB-D camera based on multiple ICP. CEUR Workshop Proceedings 2018; 2210: 300-308.
  18. Labunets V, Kokh E, Ostheimer E. Algebraic models and methods of computer image processing. Part 1. Multiplet models of multichannel images. Computer Optics 2018; 42(1): 84-95.
  19. Labunets V, Chasovskikh V, Smetanin J, Ostheimer E. Many-parameter mcomplementary Golay sequences and transforms. Computer Optics 2018; 42(6): 1074-1082.
  20. Tihonkih D, Makovetskii A, Kuznetsov V. A modified iterative closest point algorithm for shape registration. Proc SPIE 2016; 9971: 99712D.
  21. Tihonkih D, Makovetskii A, Kuznetsov V. The iterative closest points algorithm and affine transformations. CEUR Workshop Proceedings 2016; 1710: 349-356.
  22. Makovetskii A, Voronin S, Kober V, Tihonkih D. An efficient point-to-plane registration algorithm for affine transformations. Proc SPIE 2017; 10396: 103962J.
  23. Makovetskii A, Voronin S, Kober V, Tihonkih D. Affine registration of point clouds based on point-to-plane approach. Procedia Engineering 2017; 201: 322-330.
  24. Du S, Zheng N, Meng G, Yuan Z. Affine registration of point sets using ICP and ICA. IEEE Signal Proces Lett 2008; 15: 689-692.

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