Large Scale Comprehensive 3D Shape Retrieval

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-sale comprehensive 3D shape database that contains different types of models, such as generic, articulated, CAD and architecture models. The track is based on a new comprehensive 3D shape benchmark, which contains 8,987 triangle meshes that are classified into 171 categories. The benchmark was compiled as a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain-specific model types. In this track, 14 runs have been submitted by 5 groups and their retrieval accuracies were evaluated using 7 commonly used performance metrics.

[1]  Karthik Ramani,et al.  Developing an engineering shape benchmark for CAD models , 2006, Comput. Aided Des..

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

[3]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[4]  Ryutarou Ohbuchi,et al.  SHREC'12 Track: Generic 3D Shape Retrieval , 2012, 3DOR@Eurographics.

[5]  Marc Alexa,et al.  How do humans sketch objects? , 2012, ACM Trans. Graph..

[6]  Bo Li,et al.  3D model retrieval using hybrid features and class information , 2013, Multimedia Tools and Applications.

[7]  Adrien Bartoli,et al.  KAZE Features , 2012, ECCV.

[8]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[9]  Florent Perronnin,et al.  Modeling the spatial layout of images beyond spatial pyramids , 2012, Pattern Recognit. Lett..

[10]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[11]  Masaki Aono,et al.  3D shape retrieval focused on holes and surface roughness , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[12]  Thomas S. Huang,et al.  Image Classification Using Super-Vector Coding of Local Image Descriptors , 2010, ECCV.

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

[14]  Ali Shokoufandeh,et al.  Retrieving articulated 3-D models using medial surfaces , 2008, Machine Vision and Applications.

[15]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Masaki Aono,et al.  Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval , 2009, The Visual Computer.

[18]  Ming Ouhyoung,et al.  On Visual Similarity Based 3D Model Retrieval , 2003, Comput. Graph. Forum.

[19]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[21]  Longin Jan Latecki,et al.  Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval , 2009, CVPR.

[22]  Reinhard Klein,et al.  A 3D Shape Benchmark for Retrieval and Automatic Classification of Architectural Data , 2009, 3DOR@Eurographics.

[23]  Masaki Aono,et al.  A large-scale Shape Benchmark for 3D object retrieval: Toyohashi shape benchmark , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

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

[25]  Ryutarou Ohbuchi,et al.  Distance metric learning and feature combination for shape-based 3D model retrieval , 2010, 3DOR '10.

[26]  Thomas A. Funkhouser,et al.  The Princeton Shape Benchmark , 2004, Proceedings Shape Modeling Applications, 2004..

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

[28]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[29]  C. Schmid,et al.  Description of Interest Regions with Center-Symmetric Local Binary Patterns , 2006, ICVGIP.

[30]  Bin Fang,et al.  3D CAD model retrieval based on the combination of features , 2013, Multimedia Tools and Applications.