Toward a General 3-D Matching Engine: Multiple Models, Complex Scenes, and Efficient Data Filtering

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. Starting with the general matching framework introduced earlier, we present a compression scheme for spin-images; this scheme results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. In addition, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. We address efficiency and generality through two extensions to the basic matching scheme: fast filtering of scene points and processing of general data sets.

[1]  Martial Hebert,et al.  Control of Polygonal Mesh Resolution for 3-D Computer Vision , 1998, Graph. Model. Image Process..

[2]  Martial Hebert,et al.  Unconstrained registration of large 3D point sets for complex model building , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[3]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[4]  M. Hebert,et al.  The Representation, Recognition, and Locating of 3-D Objects , 1986 .

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

[6]  Shree K. Nayar,et al.  Closest point search in high dimensions , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[8]  Andrew E. Johnson,et al.  Spin-Images: A Representation for 3-D Surface Matching , 1997 .

[9]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[10]  K NayarShree,et al.  Visual learning and recognition of 3-D objects from appearance , 1995 .

[11]  K JainAnil,et al.  COSMOS-A Representation Scheme for 3D Free-Form Objects , 1997 .

[12]  Martial Hebert,et al.  A system for semi-automatic modeling of complex environments , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[13]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[14]  Wolfgang Spohn,et al.  The Representation of , 1986 .

[15]  Anil K. Jain,et al.  Recognizing geons from superquadrics fitted to range data , 1992, Image Vis. Comput..

[16]  Katsushi Ikeuchi,et al.  A spherical representation for the recognition of curved objects , 1993, 1993 (4th) International Conference on Computer Vision.

[17]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.