The correspondence framework for 3D surface matching algorithms

Beyond the inherent technical challenges, current research into the three dimensional surface correspondence problem is hampered by a lack of uniform terminology, an abundance of application specific algorithms, and the absence of a consistent model for comparing existing approaches and developing new ones. This paper addresses these challenges by presenting a framework for analysing, comparing, developing, and implementing surface correspondence algorithms. The framework uses five distinct stages to establish correspondence between surfaces. It is general, encompassing a wide variety of existing techniques, and flexible, facilitating the synthesis of new correspondence algorithms. This paper presents a review of existing surface correspondence algorithms, and shows how they fit into the correspondence framework. It also shows how the framework can be used to analyse and compare existing algorithms and develop new algorithms using the framework's modular structure. Six algorithms, four existing and two new, are implemented using the framework. Each implemented algorithm is used to match a number of surface pairs. Results demonstrate that the correspondence framework implementations are faithful implementations of existing algorithms, and that powerful new surface correspondence algorithms can be created.

[1]  E. J. Borowski The harper collins dictionary of mathematics / by E.J. Borowski and J.M. Borwein , 1991 .

[2]  John W. Tukey,et al.  Data Analysis and Regression: A Second Course in Statistics , 1977 .

[3]  Gérard G. Medioni,et al.  Structural Indexing: Efficient 3-D Object Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  M. Sharir,et al.  Partial surface and volume matching in three dimensions , 1997 .

[5]  Kjell Brunnström,et al.  Free-Form Surface Matching using Mean Field Theory , 1996, BMVC.

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

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

[8]  Naoufel Werghi,et al.  Aligning Arbitrary Surfaces using Pairwise Geometric Histograms , 1998, NMBIA.

[9]  John Williams,et al.  The Correspondence Framework for Automatic Surface Matching , 2003 .

[10]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Hon-Son Don,et al.  A Graph Matching Approach to 3-d Point Correspondences , 1991, Int. J. Pattern Recognit. Artif. Intell..

[12]  R. Wilcox Introduction to Robust Estimation and Hypothesis Testing , 1997 .

[13]  Martial Hebert,et al.  Harmonic shape images: a three-dimensional free-form surface representation and its applications in surface matching , 2000 .

[14]  Katsushi Ikeuchi,et al.  Building 3-D Models from Unregistered Range Images , 1995, CVGIP Graph. Model. Image Process..

[15]  Aly A. Farag,et al.  Free-form surface registration using surface signatures , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  P. Jonathon Phillips,et al.  Empirical Evaluation Methods in Computer Vision , 2002 .

[17]  Yi-Ping Hung,et al.  RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[19]  Andrew E. Johnson,et al.  Surface registration by matching oriented points , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

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

[21]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[22]  Chin Seng Chua,et al.  Point Signatures: A New Representation for 3D Object Recognition , 1997, International Journal of Computer Vision.

[23]  Ray Jarvis,et al.  3D free-form surface registration and object recognition , 2004, International Journal of Computer Vision.