Accurate range image registration: Eliminating or modelling outliers

Automatic and accurate range image registration is often a prerequisite step for range image analysis and interpretation. Due to occlusion, appearance and disappearance of points in different images, outliers inevitably occur. In this case, various techniques to eliminate and model outliers have been proposed for accurate range image registration. The objective of this paper is to experimentally investigate which of the outlier elimination and modelling is more effective for the evaluation of possible correspondences established, so that a deep insight into how advanced range image registration algorithms will be developed can be obtained. The experimental results based on both synthetic data and real images show that the outlier modelling often outperforms the outlier elimination in the sense of producing more accurate and robust range image registration results.

[1]  Marc Levoy,et al.  Zippered polygon meshes from range images , 1994, SIGGRAPH.

[2]  Yonghuai Liu,et al.  Automatic registration of overlapping 3D point clouds using closest points , 2006, Image Vis. Comput..

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

[4]  H. Quynh Dinh,et al.  Multi-Resolution Spin-Images , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[5]  Yonghuai Liu,et al.  Automatic 3d free form shape matching using the graduated assignment algorithm , 2005, Pattern Recognit..

[6]  Xavier Pennec,et al.  Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration , 2002, ECCV.

[7]  Ying Wang,et al.  Developing rigid motion constraints for the registration of free-form shapes , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[8]  Kari Pulli,et al.  Multiview registration for large data sets , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

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

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

[11]  Eric Mjolsness,et al.  New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence , 1998, NIPS.

[12]  Kim L. Boyer,et al.  Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Paul Newman,et al.  Using laser range data for 3D SLAM in outdoor environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..