• Publication
  • 2005
  • Issue No. 5 - May
  • Abstract - Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms
May 2005 (vol. 27 no. 5)
pp. 762-776
This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It requires prealignment of the range surfaces to a reasonable starting point and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hillclimbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GAs are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.

[1] Modeling From Reality, K. Ikeuchi and Y. Sato, eds. Kluwer Academic, 2001.
[2] L.G. Brown, “A Survey of Image Registration Techniques,” ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992.
[3] M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, and D. Fulk, “The Digital Michelangelo Project: 3D Scanning of Large Statues,” Proc. 27th Ann. Conf. Computer Graphics and Interactive Techniques, pp. 131-144, 2000.
[4] F. Bernardini, I. Martin, J. Mittleman, H. Rushmeier, and G. Taubin, “Building a Digital Model of Michelangelo's Florentine Pieta,” IEEE Computer Graphics and Applications, vol. 22, no. 1, pp. 59-67, Jan./Feb. 2002.
[5] G.C. Sharp, S.W. Lee, and D.K. Wehe, “ICP Registration Using Invariant Features,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 90-102, Jan. 2002.
[6] C. Dorai, G. Wang, A.K. Jain, and C. Mercer, “Registration and Integration of Multiple Object Views for 3D Model Construction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 83-89, Jan. 1998.
[7] G. Blais and M.D. Levine, “Registering Multiview Range Data to Create 3D Computer Objects,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 820-824, Aug. 1995.
[8] D. Huber and M. Hebert, “3D Modeling Using a Statistical Sensor Model and Stochastic Search,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 858-865, 2003.
[9] M. Reed and P. Allen, “3D Modeling from Range Imagery: An Incremental Method with a Planning Component,” Image and Vision Computing, vol. 17, no. 2, pp. 99-111, 1999.
[10] A. Stoddart and A. Hilton, “Registration of Multiple Point Sets,” Proc. IEEE Int'l Conf. Pattern Recognition, pp. 40-44, 1996.
[11] R. Bergevin, M. Soucy, H. Gagnon, and D. Laurendeau, “Towards a General Multi-View Registration Technique,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 5, pp. 540-547, May 1996.
[12] G. Turk and M. Levoy, “Zippered Polygon Meshes from Range Images,” Proc. 21st Ann. Conf. Computer Graphics, pp. 311-318, 1994.
[13] Y. Chen and G. Medioni, “Object Modeling by Registration of Multiple Range Images,” Image and Vision Computing, vol. 10, no. 3, pp. 145-155, 1992.
[14] M. Rodrigues, R. Fisher, and Y. Liu, “Special Issue on Registration and Fusion of Range Images,” Computer Vision and Image Understanding, vol. 87, nos. 1-3, pp. 1-7, 2002.
[15] A. Fitzgibbon, “Robust Registration of 2D and 3D Point Sets,” Proc. British Machine Vision Conf., pp. 662-670, 2001.
[16] A. Sappa, A. Restrepo-Specht, and M. Devy, “Range Image Registration by Using an Edge-Based Representation,” Proc. Int'l Symp. Intelligent Robotic Systems, pp. 167-176, 2001.
[17] O. Faugeras and M. Hebert, “The Representation, Recognition, and Locating of 3D Objects,” Int'l J. Robotics Research, vol. 5, no. 3, pp. 27-52, 1986.
[18] J. Wyngaerd and L. Van Gool, “Automatic Crude Patch Registration: Toward Automatic 3D Model Building,” Computer Vision and Image Understanding, vol. 87, nos. 1-3, pp. 8-26, 2002.
[19] S. Yamany and A. Farag, “Free-Form Surface Registration Using Surface Signatures,” Proc. Int'l Conf. Computer Vision, pp. 1098-1104, 1999.
[20] C. Chua and R. Jarvis, “3D Free-Form Surface Registration and Object Recognition,” Int'l J. Computer Vision, vol. 17, no. 1, pp. 77-99, 1996.
[21] A. Johnson and M. Hebert, “Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433-449, May 1999.
[22] C. Chen, Y. Hung, and J. Cheng, “RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1229-1234, Nov. 1999.
[23] P.J. Besl and N.D. McKay, “A Method for Registration of 3-D Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[24] S. Rusinkiewicz and M. Levoy, “Efficient Variants of the ICP Algorithm,” Proc. Third Int'l Conf. 3-D Digital Imaging and Modeling, vol. 1, pp. 145-152, 2001.
[25] G. Dalley and P. Flynn, “Pair-Wise Range Image Registration: A Study in Outlier Classification,” Computer Vision and Image Understanding, vol. 87, no. 1, pp. 104-115, 2002.
[26] K.F. Man, K.S. Tang, and S. Kwong, “Genetic Algorithms: Concepts and Applications,” IEEE Trans. Industrial Electronics, vol. 43, no. 5, pp. 519-534, 1996.
[27] S. Kirkpatrick, C. Gelatt, and M. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 671-680, 1983.
[28] D. Simon, M. Hebert, and T. Kanade, “Techniques for Fast and Accurate Intrasurgical Registration,” J. Image Guided Surgery, vol. 1, no. 1, pp. 17-29, 1995.
[29] G. Champleboux, S. Lavallee, R. Szeliski, and L. Brunie, “From Accurate Range Imaging Sensor Calibration to Accurate Model-Based 3-D Object Localization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 83-88, 1992.
[30] P. Chalermwat and T. El-Ghazawi, “Multi-Resolution Image Registration Using Genetics,” Proc. Sixth IEEE Int'l Conf. Image Processing, vol. 2, pp. 452-456, 1999.
[31] M. Ahmed, S. Yamany, E. Hemayed, S. Ahmed, S. Roberts, and A. Farag, “3D Reconstruction of the Human Jaw from a Sequence of Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 646-653, 1997.
[32] K. Brunnstrom and A.J. Stoddart, “Genetic Algorithms for Free-Form Surface Matching,” Proc. 13th Int'l Conf. Pattern Recognition, vol. 4, pp. 689-693, 1996.
[33] C. Robertson and R.B. Fisher, “Parallel Evolutionary Registration of Range Data,” Computer Vision and Image Understanding, vol. 87, no. 1, pp. 39-50, 2002.
[34] S. Park and M. Subbarao, “A New Technique for Registration and Integration of Partial 3D Models,” Proc. SPIE Conf., vol. 4567, pp. 65-74, 2001.
[35] G. Dalley and P. Flynn, “Range Image Registration: A Software Platform and Empirical Evaluation,” Proc. Third Int'l Conf. 3-D Digital Imaging and Modeling, vol. 1, pp. 246-253, 2001.
[36] Z.Y. Zhang, “Iterative Point Matching for Registration of Free-Form Curves and Surfaces,” Int'l J. Computer Vision, vol. 13, no. 2, pp. 119-152, 1994.
[37] R. D'Agostino, Tests for the Normal Distribution. pp. 367-419, Marcel Dekker, 1986.
[38] J. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor: The Univ. of Michigan Press, 1975.
[39] D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman, 1989.
[40] P.H.S. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer Vision and Image Understanding, vol. 78, no. 1, pp. 138-156, 2000.
[41] P.F.U. Gotardo, O.R.P. Bellon, and L. Silva, “Range Image Segmentation by Surface Extraction Using an Improved Robust Estimator,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.
[42] P.F. U. Gotardo, O.R. P. Bellon, K.L. Boyer, and L. Silva, “Range Image Segmentation into Planar and Quadric Surfaces Using an Improved Robust Estimator and Genetic Algorithm,” IEEE Trans. Systems, Man, and Cybernetics, Part B, 2004.
[43] J.M. Renders and S.P. Flasse, “Hybrid Methods Using Genetic Algorithms for Global Optimization,” IEEE Trans. Systems, Man, and Cybernetics Part B-Cybernetics, vol. 26, no. 2, pp. 243-258, 1996.
[44] L. Ingber, “Very Fast Simulated Re-Annealing,” Math. Computer Modelling, vol. 12, pp. 967-973, 1989.
[45] T. Masuda and N. Yokoya, “A Robust Method for Registration and Segmentation of Multiple Range Images,” Computer Vision and Image Understanding, vol. 10, no. 3, pp. 295-307, 1995.

Index Terms:
Range image registration, genetic algorithms, robust methods, stochastic search.
Citation:
Luciano Silva, Olga R.P. Bellon, Kim L. Boyer, "Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, May 2005, doi:10.1109/TPAMI.2005.108
Usage of this product signifies your acceptance of the Terms of Use.