Improved spin images for 3D surface matching using signed angles

Despite the popularity of spin images in surface matching and registration, disadvantages such as noise sensitivity and low discriminative ability still hindered their usefulness in real applications. In this paper, a novel approach was proposed for improving the spin images. The proposed method modified the standard spin images by using angle information between the normals of reference point and neighboring points. This information largely increased the robustness to noise without losing the intrinsic advantages of spin images. Moreover, signs were defined to incorporate the directions of angles which were shown to be able to further improve the descriptive power. Experiments were also conducted to show the outperformance of improved spin images under different levels of noise, and good agreements were obtained by comparing with the standard spin images and a recent popular 3D descriptor.

[1]  Andrew E. Johnson,et al.  Recognizing objects by matching oriented points , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[3]  Martial Hebert,et al.  Large data sets and confusing scenes in 3-D surface matching and recognition , 1999, Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062).

[4]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Karthik Ramani,et al.  Three-dimensional shape searching: state-of-the-art review and future trends , 2005, Comput. Aided Des..

[6]  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).

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

[8]  Guido M. Cortelazzo,et al.  3D registration by textured spin-images , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

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

[10]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[11]  Andrew E. Johnson,et al.  Efficient multiple model recognition in cluttered 3-D scenes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  Andrew E. Johnson,et al.  Surface landmark selection and matching in natural terrain , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[13]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.