ShapeNet: An Information-Rich 3D Model Repository

We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.

[1]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[2]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[3]  M. L. Jones,et al.  PDBsum: a Web-based database of summaries and analyses of all PDB structures. , 1997, Trends in biochemical sciences.

[4]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[5]  Dietmar Saupe,et al.  3D Model Retrieval , 2001 .

[6]  Greg Turk,et al.  Simplification and Repair of Polygonal Models Using Volumetric Techniques , 2003, IEEE Trans. Vis. Comput. Graph..

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

[8]  Ali Shokoufandeh,et al.  Retrieving Articulated 3-D Models Using Medial Surfaces and Their Graph Spectra , 2005, EMMCVPR.

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

[10]  R. Veltkamp,et al.  SHREC 2007 3 D Shape Retrieval Contest , 2006 .

[11]  R. Veltkamp,et al.  SHREC 2007 3 D Shape Retrieval Contest , 2006 .

[12]  Thomas A. Funkhouser,et al.  A benchmark for 3D mesh segmentation , 2009, ACM Trans. Graph..

[13]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[14]  Antonio Torralba,et al.  Building a database of 3D scenes from user annotations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[16]  Antonio Torralba,et al.  LabelMe: Online Image Annotation and Applications , 2010, Proceedings of the IEEE.

[17]  Cordelia Schmid,et al.  Multi-view object class detection with a 3D geometric model , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Vladimir G. Kim,et al.  Möbius Transformations For Global Intrinsic Symmetry Analysis , 2010, Comput. Graph. Forum.

[19]  Pat Hanrahan,et al.  Example-based synthesis of 3D object arrangements , 2012, ACM Trans. Graph..

[20]  Siddhartha Chaudhuri,et al.  A probabilistic model for component-based shape synthesis , 2012, ACM Trans. Graph..

[21]  Stephen DiVerdi,et al.  Exploring collections of 3D models using fuzzy correspondences , 2012, ACM Trans. Graph..

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

[23]  Niloy J. Mitra,et al.  Symmetry in 3D Geometry: Extraction and Applications , 2013, Comput. Graph. Forum.

[24]  Thomas A. Funkhouser,et al.  Schelling points on 3D surface meshes , 2012, ACM Trans. Graph..

[25]  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.

[26]  Andrew Owens,et al.  SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels , 2013, 2013 IEEE International Conference on Computer Vision.

[27]  Stephen DiVerdi,et al.  Learning part-based templates from large collections of 3D shapes , 2013, ACM Trans. Graph..

[28]  Leonidas J. Guibas,et al.  Fine-grained semi-supervised labeling of large shape collections , 2013, ACM Trans. Graph..

[29]  Céline Loscos,et al.  3D Model Retrieval , 2013 .

[30]  Jonathan Krause,et al.  3D Object Representations for Fine-Grained Categorization , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[31]  Niloy J. Mitra,et al.  Creating consistent scene graphs using a probabilistic grammar , 2014, ACM Trans. Graph..

[32]  Silvio Savarese,et al.  Beyond PASCAL: A benchmark for 3D object detection in the wild , 2014, IEEE Winter Conference on Applications of Computer Vision.

[33]  Jianxiong Xiao,et al.  Sliding Shapes for 3D Object Detection in Depth Images , 2014, ECCV.

[34]  Bin Fang,et al.  SHREC'14 Track: Large Scale Comprehensive 3D Shape Retrieval , 2014 .

[35]  Pat Hanrahan,et al.  On being the right scale: sizing large collections of 3D models , 2014, SIGGRAPH ASIA Indoor Scene Understanding Where Graphics Meets Vision.

[36]  Pat Hanrahan,et al.  Semantically-enriched 3D models for common-sense knowledge , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[37]  Jianxiong Xiao,et al.  3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).