Spin Contour

Spin image is a powerful shape descriptor, useful in a point set or surface registration. However, the usage of spin images is hampered by issues such as sensitivity to noise and sampling rate and time-consuming matching process. We propose a novel spin-image-based local surface descriptor named spin contour to alleviate these problems. This descriptor is not an image but a 2-D point set. Comparisons show that the spin contour is robust to noise and sampling differences. The matching time is also improved over spin images.

[1]  Alberto Del Bimbo,et al.  Content-Based Retrieval of 3-D Objects Using Spin Image Signatures , 2007, IEEE Transactions on Multimedia.

[2]  Ko Nishino,et al.  3D Geometric Scale Variability in Range Images: Features and Descriptors , 2012, International Journal of Computer Vision.

[3]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Martial Hebert,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).

[5]  Daniel Cremers,et al.  The wave kernel signature: A quantum mechanical approach to shape analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[6]  Ko Nishino,et al.  Scale-Dependent 3D Geometric Features , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[7]  Mohammed Bennamoun,et al.  An Accurate and Robust Range Image Registration Algorithm for 3D Object Modeling , 2014, IEEE Transactions on Multimedia.

[8]  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).

[9]  Hui Chen,et al.  3D free-form object recognition in range images using local surface patches , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[10]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[11]  Ghassan Hamarneh,et al.  A Survey on Shape Correspondence , 2011, Comput. Graph. Forum.

[12]  Alexander M. Bronstein,et al.  Volumetric heat kernel signatures , 2010, 3DOR '10.

[13]  Alexander M. Bronstein,et al.  Affine-invariant geodesic geometry of deformable 3D shapes , 2010, Comput. Graph..

[14]  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).

[15]  Ko Nishino,et al.  Scale-hierarchical 3D object recognition in cluttered scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Sim Heng Ong,et al.  Improved spin images for 3D surface matching using signed angles , 2012, 2012 19th IEEE International Conference on Image Processing.

[17]  Michael G. Strintzis,et al.  Fast content-based search of VRML models based on shape descriptors , 2005, IEEE Transactions on Multimedia.

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

[19]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[20]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[21]  Leonidas J. Guibas,et al.  A concise and provably informative multi-scale signature based on heat diffusion , 2009 .

[22]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[23]  Matthias Zwicker,et al.  Automatic Registration for Articulated Shapes , 2008, Comput. Graph. Forum.

[24]  Ko Nishino,et al.  Scale-Dependent/Invariant Local 3D Shape Descriptors for Fully Automatic Registration of Multiple Sets of Range Images , 2008, ECCV.

[25]  Patrick J. Flynn,et al.  Recognition of Free-Form Objects in Dense Range Data Using Local Features , 2002, ICPR.

[26]  Hamid Laga,et al.  Covariance-Based Descriptors for Efficient 3D Shape Matching, Retrieval, and Classification , 2015, IEEE Transactions on Multimedia.

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

[28]  Matthias Zwicker,et al.  Global registration of dynamic range scans for articulated model reconstruction , 2011, TOGS.

[29]  Matthias Zwicker,et al.  Range Scan Registration Using Reduced Deformable Models , 2009, Comput. Graph. Forum.

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

[31]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[33]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[34]  Wojciech Matusik,et al.  Articulated mesh animation from multi-view silhouettes , 2008, ACM Trans. Graph..

[35]  Iasonas Kokkinos,et al.  Scale-invariant heat kernel signatures for non-rigid shape recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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