Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification

A statistical representation of three-dimensional shapes is introduced, based on a novel four-dimensional feature. The feature parameterizes the intrinsic geometrical relation of an oriented surface-point pair. The set of all such features represents both local and global characteristics of the surface. We compress this set into a histogram. A database of histograms, one per object, is sampled in a training phase. During recognition, sensed surface data, as may be acquired by stereo vision, a laser range-scanner, etc., are processed and compared to the stored histograms. We evaluate the match quality by six different criteria that are commonly used in statistical settings. Experiments with artificial data containing varying levels of noise and occlusion of the objects show that Kullback-Leibler and likelihood matching yield robust recognition rates. We propose histograms of the geometric relation between two oriented surface points (surflets) as a compact yet distinctive representation of arbitrary three-dimensional shapes.

[1]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[2]  Gerd Hirzinger,et al.  Probabilistic Search for Object Segmentation and Recognition , 2002, ECCV.

[3]  Mohamed Daoudi,et al.  A practical approach for 3D model indexing by combining local and global invariants , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[4]  Ilan Shimshoni,et al.  Using principal curvatures and Darboux frame to recover 3D geometric primitives from range images , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

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

[6]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[7]  Patrick J. Flynn,et al.  A Survey Of Free-Form Object Representation and Recognition Techniques , 2001, Comput. Vis. Image Underst..

[8]  Bastian Leibe,et al.  Local feature histograms for object recognition from range images , 2001 .

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Aly A. Farag,et al.  Surfacing Signatures: An Orientation Independent Free-Form Surface Representation Scheme for the Purpose of Objects Registration and Matching , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Bernt Schiele,et al.  3D object recognition from range images using local feature histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.