Orientation invariant 3D object classification using hough transform based methods

In comparison to the 2D case, object class recognition in 3D is a much less researched area. However, with the advent of affordable 3D acquisition technology and the growing popularity of 3D content, its relevance is steadily increasing. Just as in the 2D case, 3D data is often partial, noisy and without prior segmentation. Moreover, the object is rarely observed in a canonical frame of reference with respect to orientation (or scale). We propose a method, using Hough-voting for local 3D features, which is orientation (and scale) invariant.

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

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

[3]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Szymon Rusinkiewicz,et al.  Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors , 2003, Symposium on Geometry Processing.

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

[6]  Benjamin B. Kimia,et al.  A Similarity-Based Aspect-Graph Approach to 3D Object Recognition , 2004, International Journal of Computer Vision.

[7]  Leonidas J. Guibas,et al.  Robust global registration , 2005, SGP '05.

[8]  Thomas A. Funkhouser,et al.  Partial matching of 3D shapes with priority-driven search , 2006, SGP '06.

[9]  Mohammed Bennamoun,et al.  Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[11]  Luc Van Gool,et al.  An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.

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

[13]  Petros Daras,et al.  SHREC 2009 - Shape Retrieval Contest of Partial 3D Models | NIST , 2009 .

[14]  Jitendra Malik,et al.  Object detection using a max-margin Hough transform , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Ryutarou Ohbuchi,et al.  Dense sampling and fast encoding for 3D model retrieval using bag-of-visual features , 2009, CIVR '09.

[16]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

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

[18]  Leonidas J. Guibas,et al.  Shape Google: a computer vision approach to isometry invariant shape retrieval , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[19]  Andrea Fusiello,et al.  A Bag of Words Approach for 3D Object Categorization , 2009, MIRAGE.

[20]  Alexander M. Bronstein,et al.  Numerical Geometry of Non-Rigid Shapes , 2009, Monographs in Computer Science.

[21]  Luc Van Gool,et al.  Feature-centric Efficient Subwindow Search , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Luc Van Gool,et al.  Hough Transform and 3D SURF for Robust Three Dimensional Classification , 2010, ECCV.

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.