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Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error
Aug. 2012 (vol. 34 no. 8)
pp. 1520-1532
ASCII Text
x 
 
H. Meinzer, M. Fangerau, M. Schmidt, J. M. Fitzpatrick, T. R. dos Santos, A. M. Franz, L. Maier-Hein, "Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1520-1532, Aug., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.248,
author = {H. Meinzer and M. Fangerau and M. Schmidt and J. M. Fitzpatrick and T. R. dos Santos and A. M. Franz and L. Maier-Hein},
title = {Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {8},
issn = {0162-8828},
year = {2012},
pages = {1520-1532},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.248},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error
IS - 8
SN - 0162-8828
SP1520
EP1532
EPD - 1520-1532
A1 - H. Meinzer,
A1 - M. Fangerau,
A1 - M. Schmidt,
A1 - J. M. Fitzpatrick,
A1 - T. R. dos Santos,
A1 - A. M. Franz,
A1 - L. Maier-Hein,
PY - 2012
KW - solid modelling
KW - computational geometry
KW - convergence of numerical methods
KW - covariance matrices
KW - image registration
KW - iterative methods
KW - medical image processing
KW - mesh generation
KW - point-based surface registration
KW - convergent iterative closest-point algorithm
KW - ICP algorithm
KW - anisotropic localization error
KW - inhomogenous localization error
KW - geometric alignment
KW - 3D models
KW - point correspondences
KW - data alignment
KW - isotropic Gaussian noise
KW - partial overlapping surfaces
KW - covariance matrices
KW - surface meshes
KW - mesh extraction
KW - medical imaging data
KW - partial surface registration
KW - Iterative closest point algorithm
KW - Measurement
KW - Covariance matrix
KW - Cameras
KW - Three dimensional displays
KW - Noise
KW - Algorithm design and analysis
KW - anisotropic weighting.
KW - Registration
KW - surface algorithms
KW - ICP
KW - point-based registration
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
H. Meinzer, Div. of Med. & Biol. Inf., German Cancer Res. Center (DKFZ), Heidelberg, Germany
M. Fangerau, Div. of Med. & Biol. Inf., German Cancer Res. Center (DKFZ), Heidelberg, Germany
M. Schmidt, Digital Image Process. Group, Univ. of Heidelberg, Heidelberg, Germany
J. M. Fitzpatrick, Dept. of Electr. Eng. & Comput. Sci., Vanderbilt Univ., Nashville, TN, USA
T. R. dos Santos, Div. of Med. & Biol. Inf., German Cancer Res. Center (DKFZ), Heidelberg, Germany
A. M. Franz, Div. of Med. & Biol. Inf., German Cancer Res. Center (DKFZ), Heidelberg, Germany
L. Maier-Hein, Div. of Med. & Biol. Inf., German Cancer Res. Center (DKFZ), Heidelberg, Germany
Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. From a statistical point of view, however, it implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we show that this assumption may lead to errors and generalize the ICP such that it can account for anisotropic and inhomogenous localization errors. We 1) provide a formal description of the algorithm, 2) extend it to registration of partially overlapping surfaces, 3) prove its convergence, 4) derive the required covariance matrices for a set of selected applications, and 5) present means for optimizing the runtime. An evaluation on publicly available surface meshes as well as on a set of meshes extracted from medical imaging data shows a dramatic increase in accuracy compared to the original ICP, especially in the case of partial surface registration. As point-based surface registration is a central component in various applications, the potential impact of the proposed method is high.

[1] P. Yan and K.W. Bowyer, "Biometric Recognition Using Three-Dimensional Ear Shape," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1297-1308, Aug. 2007.
[2] L. Armesto, J. Minguez, and L. Montesano, "A Generalization of the Metric-Based Iterative Closest Point Technique for 3D Scan Matching," Proc. IEEE Int'l Conf. Robotics and Automation, pp. 1367-1372, 2010.
[3] D.M. Cash, M.I. Miga, S.C. Glasgow, B.M. Dawant, L.W. Clements, Z. Cao, R.L. Galloway, and W.C. Chapman, "Concepts and Preliminary Data Toward the Realization of Image-Guided Liver Surgery," J. Gastrointestinal Surgery, vol. 11, pp. 844-859, 2007.
[4] E.M. Bispo and R.B. Fisher, "Free-Form Surface Matching for Surface Inspection," Proc. Sixth IMA Conf. Math. of Surfaces, pp. 119-136, 1994.
[5] O. van Kaick, H. Zhang, G. Hamarneh, and D. Cohen-Or, "A Survey on Shape Correspondence," Computer Graphics Forum, vol. 30, no. 6, pp. 1681-1707, 2011.
[6] P.J. Besl and N.D. McKay, "A Method for Registration of 3D Shapes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[7] Y. Chen and G. Medioni, "Object Modeling by Registration of Multiple Range Images," Int'l J. Computer Vision and Image Understanding, vol. 10, pp. 145-155, 1992.
[8] R. Lange, "3D Time-of-Flight Distance Measurement with Custom Solid-State Image Sensors in CMOS/CCD-Technology," PhD thesis, Univ. of Siegen, 2000.
[9] R. Balachandran and J.M. Fitzpatrick, "Iterative Solution for Rigid-Body Point-Based Registration with Anisotropic Weighting," Proc. SPIE, vol. 7261, no. 1, pp. 72613D-1-72613D-10, 2009.
[10] R.S.J. Estépar, A. Brun, and C.-F. Westin, "Robust Generalized Total Least Squares Iterative Closest Point Registration," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2004.
[11] N. Ohta and K. Kanatani, "Optimal Estimation of Three-Dimensional Rotation and Reliability Evaluation," Proc. Fifth European Conf. Computer Vision, pp. 175-187, 1998.
[12] L. Maier-Hein, T.R. dos Santos, A.M. Franz, H.-P. Meinzer, and J.M. Fitzpatrick, "Iterative Closest Point Algorithm with Anisotropic Weighting and Its Application to Fine Surface Registration," Proc. SPIE, vol. 7962, pp. 79620 W-1-79620 W-9, 2011.
[13] S. Rusinkiewicz and M. Levoy, "Efficient Variants of the ICP Algorithm," Proc. Third Int'l Conf. 3D Digital Imaging and Modeling, pp. 145-152, 2001.
[14] G. Turk and M. Levoy, "Zippered Polygon Meshes from Range Images," Proc. ACM Siggraph, pp. 311-318, 1994.
[15] T. Masuda, K. Sakaue, and N. Yokoya, "Registration and Integration of Multiple Range Images for 3D Model Construction," Proc. 13th Int'l Conf. Pattern Recognition, pp. 879-883, 1996.
[16] S. Weik, "Registration of 3D Partial Surface Models Using Luminance and Depth Information," Proc. Int'l Conf. Recent Advances in 3D Digital Imaging and Modeling, pp. 93-100, 1997.
[17] G. Godin, M. Rioux, and R. Baribeau, "Three-Dimensional Registration Using Range and Intensity Information," Proc. SPIE, vol. 2350, pp. 279-290, 1994.
[18] J. Feldmar and N. Ayache, "Rigid, Affine and Locally Affine Registration of Free-Form Surfaces," Int'l J. Computer Vision, vol. 18, no. 2, pp. 99-119, 1996.
[19] 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.
[20] K. Pulli, "Multiview Registration for Large Data Sets," Proc. Second Int'l Conf. 3D Digital Imaging and Modeling, pp. 160-168, 1999.
[21] S. Granger, X. Pennec, and A. Roche, "Rigid Point-Surface Registration Using an EM Variant of ICP for Computer Guided Oral Implantology," Proc. Fourth Int'l Conf. Medical Image Computing and Computer-Assisted Intervention, pp. 752-761, 2001.
[22] S. Kaneko, T. Kondo, and A. Miyamoto, "Robust Matching of 3D Contours Using Iterative Closest Point Algorithm Improved by M-Estimation," Pattern Recognition, vol. 36, pp. 2041-2047, 2003.
[23] 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.
[24] P.J. Neugenbauer, "Geometrical Cloning of 3D Objects via Simultaneous Registration of Multiple Range Images," Proc. Int'l Conf. Shape Modeling and Applications, pp. 130-139, 1997.
[25] 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.
[26] M.F. Hansen, M.R. Blas, and R. Larsen, "Mahalanobis Distance Based Iterative Closest Point," Proc. SPIE, vol. 6512, p. 65121Y, Feb. 2007.
[27] P.C. Mahalanobis, "On the Generalized Distance in Statistics," Proc. Nat'l Inst. of Science of Calcutta, vol. 2, pp. 49-55, 1936.
[28] D. Chetverikov, D. Stepanov, and P. Krsek, "Robust Euclidean Alignment of 3D Point Sets: The Trimmed Iterative Closest Point Algorithm," Image and Vision Computing, vol. 23, pp. 299-309, 2005.
[29] B.K.P. Horn, "Closed-Form Solution of Absolute Orientation Using Unit Quaternions," J. Optical Soc. Am. A, vol. 4, pp. 629-642, 1987.
[30] A.E. Johnson and S.B. Kang, "Registration and Integration of Textured 3D Data," Proc. Int'l Conf. Recent Advances in 3D Digital Imaging and Modeling, pp. 234-241, 1997.
[31] T. Jost and H. Hügli, "A Multi-Resolution ICP with Heuristic Closest Point Search for Fast and Robust 3D Registration of Range Images," Proc. Fourth Int'l Conf. 3D Digital Imaging and Modeling, pp. 427-433, 2003.
[32] D. Simon, "Fast and Accurate Shape-Based Registration," PhD thesis, Robotics Inst., Carnegie Mellon Univ., Dec. 1996.
[33] G.P. Penney, P.J. Edwards, A.P. King, J.M. Blackall, P.G. Batchelor, and D.J. Hawkes, "A Stochastic Iterative Closest Point Algorithm (StochastICP)," Proc. Fourth Int'l Conf. Medical Image Computing and Computer-Assisted Intervention, pp. 762-769, 2001.
[34] T. Tamaki, M. Abe, B. Raytchev, and K. Kaneda, "Softassign and EM-ICP on GPU," Proc. First Int'l Conf. Networking and Computing, pp. 179-183, 2010.
[35] C. Langis, M. Greenspan, and G. Godin, "The Parallel Iterative Closest Point Algorithm," Proc. Third Int'l Conf. 3D Digital Imaging and Modeling, pp. 195-202, 2001.
[36] B. Combès and S. Prima, "Prior Affinity Measures on Matches for ICP-Like Nonlinear Registration of Free-Form Surfaces," Proc. IEEE Sixth Int'l Conf. Symp. Biomedical Imaging, pp. 370-373, 2009.
[37] D. Münch, B. Combès, and S. Prima, "A Modified ICP Algorithm for Normal-Guided Surface Registration," Proc. SPIE, vol. 7623, no. 7, pp. 76231A-1-76231A-8, 2010.
[38] H. Chui and A. Rangarajan, "A New Point Matching Algorithm for Non-Rigid Registration," Computer Vision and Image Understanding, vol. 89, pp. 114-141, Feb. 2003.
[39] B. Amberg, S. Romdhani, and T. Vetter, "Optimal Step Nonrigid ICP Algorithms for Surface Registration," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[40] A. Danilchenko and J. Fitzpatrick, "General Approach to First-Order Error Prediction in Rigid Point Registration," IEEE Trans. Medical Imaging, vol. 30, no. 3, pp. 679-693, Mar. 2011.
[41] L. Maier-Hein, M. Schmidt, A. Franz, T. dos Santos, A. Seitel, B. Jähne, J. Fitzpatrick, and H. Meinzer, "Accounting for Anisotropic Noise in Fine Registration of Time-of-Flight Range Data with High-Resolution Surface Data," Proc. Medical Image Computing and Computer Assisted Intervention, pp. 251-258, 2010.
[42] A. Myronenko and X. Song, "Point Set Registration: Coherent Point Drift," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2262-2275, Dec. 2010.

Index Terms:
solid modelling,computational geometry,convergence of numerical methods,covariance matrices,image registration,iterative methods,medical image processing,mesh generation,point-based surface registration,convergent iterative closest-point algorithm,ICP algorithm,anisotropic localization error,inhomogenous localization error,geometric alignment,3D models,point correspondences,data alignment,isotropic Gaussian noise,partial overlapping surfaces,covariance matrices,surface meshes,mesh extraction,medical imaging data,partial surface registration,Iterative closest point algorithm,Measurement,Covariance matrix,Cameras,Three dimensional displays,Noise,Algorithm design and analysis,anisotropic weighting.,Registration,surface algorithms,ICP,point-based registration
Citation:
H. Meinzer, M. Fangerau, M. Schmidt, J. M. Fitzpatrick, T. R. dos Santos, A. M. Franz, L. Maier-Hein, "Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1520-1532, Aug. 2012, doi:10.1109/TPAMI.2011.248
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