SHREC'13 Track: Retrieval on Textured 3D Models

This contribution reports the results of the SHREC 2013 track: Retrieval on Textured 3D Models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 240 textured mesh models, divided into 10 classes. Each model has been used in turn as a query against the remaining part of the database. For a given query, the goal was to retrieve the most similar objects. The track saw six participants and the submission of eleven runs.

[1]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[4]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

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

[6]  Marco Attene,et al.  ReMESH: An Interactive Environment to Edit and Repair Triangle Meshes , 2006, IEEE International Conference on Shape Modeling and Applications 2006 (SMI'06).

[7]  Ryutarou Ohbuchi,et al.  Salient local visual features for shape-based 3D model retrieval , 2008, 2008 IEEE International Conference on Shape Modeling and Applications.

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

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

[10]  Michael Werman,et al.  Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Martha Elizabeth Shenton,et al.  Laplace-Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis , 2009, Comput. Aided Des..

[12]  Paul Suetens,et al.  Isometric Deformation Modelling for Object Recognition , 2009, CAIP.

[13]  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.

[14]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Afzal Godil,et al.  Visual Similarity Based 3D Shape Retrieval Using Bag-of-Features , 2010, 2010 Shape Modeling International Conference.

[16]  Leonidas J. Guibas,et al.  Shape google: Geometric words and expressions for invariant shape retrieval , 2011, TOGS.

[17]  Yong-Jin Liu,et al.  3D model retrieval based on color + geometry signatures , 2011, The Visual Computer.

[18]  Andrea Giachetti,et al.  Radial Symmetry Detection and Shape Characterization with the Multiscale Area Projection Transform , 2012, Comput. Graph. Forum.