Evaluating collinearity constraint for automatic range image registration

While most of the existing range image registration algorithms either have to extract and match structural (geometric or optical) features or have to estimate the motion parameters of interest from outliers corrupted point correspondence data for the elimination of false matches in the process of image registration, the registration error and the collinearity error derived directly from the traditional closest point criterion are also capable of doing the same job. However, the latter has an advantage of easy implementation. The purpose of this paper is to investigate which definition of collinearity is more accurate and stable in eliminating false matches inevitably introduced by the closest point criterion. The experiments based on real images show the advantages and disadvantages of different definitions of collinearity.

[1]  Andrew E. Johnson,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[2]  Atsushi Nakazawa,et al.  Iterative refinement of range images with anisotropic error distribution , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Martial Hebert,et al.  Fully automatic registration of multiple 3D data sets , 2003, Image Vis. Comput..

[4]  Marc Levoy,et al.  Geometrically stable sampling for the ICP algorithm , 2003, Fourth International Conference on 3-D Digital Imaging and Modeling, 2003. 3DIM 2003. Proceedings..

[5]  Marcos A. Rodrigues,et al.  Geometrical Analysis of Two Sets of 3D Correspondence Data Patterns for the Registration of Free-Form Shapes , 2002, J. Intell. Robotic Syst..

[6]  Ross T. Whitaker,et al.  Indoor scene reconstruction from sets of noisy range images , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[7]  Sang Wook Lee,et al.  ICP Registration Using Invariant Features , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Hong Zhou,et al.  Projecting registration error for accurate registration of overlapping range images , 2006, Robotics Auton. Syst..

[9]  Yonghuai Liu,et al.  Improving ICP with easy implementation for free-form surface matching , 2004, Pattern Recognit..

[10]  Martin D. Levine,et al.  Registering Multiview Range Data to Create 3D Computer Objects , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Sang Wook Lee,et al.  Invariant features and the registration of rigid bodies , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[12]  Robert B. Fisher,et al.  Finding Surface Correspondance for Object Recognition and Registration Using Pairwise Geometric Histograms , 1998, ECCV.

[13]  Ross T. Whitaker,et al.  A Maximum-Likelihood Surface Estimator for Dense Range Data , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Radu Horaud,et al.  Hand Motion from 3D Point Trajectories and a Smooth Surface Model , 2004, ECCV.

[15]  Yonghuai Liu,et al.  3D shape matching using collinearity constraint , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  Luc Van Gool,et al.  Matching of 3-D curves using semi-differential invariants , 1995, Proceedings of IEEE International Conference on Computer Vision.

[17]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Sing Bing Kang,et al.  Registration and integration of textured 3-D data , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[20]  Bodo Rosenhahn,et al.  Performance of Constraint Based Pose Estimation Algorithms , 2000, DAGM-Symposium.

[21]  Marc Levoy,et al.  Efficient variants of the ICP algorithm , 2001, Proceedings Third International Conference on 3-D Digital Imaging and Modeling.

[22]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.