Erratum to: AB3D: action-based 3D descriptor for shape analysis

High-level geometry processing has been a hot topic in graphics community. The functionality analysis of 3D models is an essential issue in this area. Existing 3D models often exhibit both large intra-class and inter-class variations in shape geometry and topology, making the consistent analysis of functionality challenging. Traditional 3D shape analysis methods which rely on geometric shape descriptors can not obtain satisfying results on these 3D models. We develop a new 3D shape descriptor based on the interactions between 3D models and virtual human actions, which is called Action-Based 3D Descriptor (AB3D). Due to the implied semantic meanings of virtual human actions, we obtain encouraging results on consistent segmentation based on AB3D. Finally, we present a method for recognition and reconstruction of scanned 3D indoor scenes using our AB3D. Experiments show that AB3D is a promising shape descriptor toward functionality analysis of 3D shapes.

[1]  Daniel Cohen-Or,et al.  Fit and diverse , 2012, ACM Trans. Graph..

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Jun Li,et al.  Symmetry Hierarchy of Man‐Made Objects , 2011, Comput. Graph. Forum.

[4]  Daniel Cohen-Or,et al.  Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering , 2011, ACM Trans. Graph..

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

[6]  Jakob Andreas Bærentzen,et al.  3D distance fields: a survey of techniques and applications , 2006, IEEE Transactions on Visualization and Computer Graphics.

[7]  Ligang Liu,et al.  Co‐Segmentation of 3D Shapes via Subspace Clustering , 2012, Comput. Graph. Forum.

[8]  J. van Leeuwen,et al.  Discrete Geometry for Computer Imagery , 2003, Lecture Notes in Computer Science.

[9]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[10]  Daniel Thalmann,et al.  Bridging Geometry and Semantics for Object Manipulation and Grasping , 2005 .

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

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

[13]  Silvia Biasotti,et al.  Extended Reeb Graphs for Surface Understanding and Description , 2000, DGCI.

[14]  M. Levas.  OBBTree : A Hierarchical Structure for Rapid Interference Detection , .

[15]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[16]  Kai Xu,et al.  Partial intrinsic reflectional symmetry of 3D shapes , 2009, SIGGRAPH 2009.

[17]  Remco C. Veltkamp,et al.  SHREC'10 Track: Large Scale Retrieval , 2010, 3DOR@Eurographics.

[18]  Ehud Rivlin,et al.  Functional 3D Object Classification Using Simulation of Embodied Agent , 2006, BMVC.

[19]  Nathan Silberman,et al.  Indoor scene segmentation using a structured light sensor , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[20]  Marco Attene,et al.  Characterization of 3D shape parts for semantic annotation , 2009, Comput. Aided Des..

[21]  Daniel Cohen-Or,et al.  Structure-aware shape processing , 2013, Eurographics.

[22]  D. Cohen-Or,et al.  Upright orientation of man-made objects , 2008, SIGGRAPH 2008.

[23]  Luc Van Gool,et al.  What makes a chair a chair? , 2011, CVPR 2011.

[24]  FunkhouserThomas,et al.  Learning part-based templates from large collections of 3D shapes , 2013 .

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

[26]  Larry S. Davis,et al.  Objects in Action: An Approach for Combining Action Understanding and Object Perception , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Martin Reuter,et al.  Hierarchical Shape Segmentation and Registration via Topological Features of Laplace-Beltrami Eigenfunctions , 2010, International Journal of Computer Vision.

[28]  Marco Attene,et al.  Computational methods for understanding 3D shapes , 2006, Comput. Graph..

[29]  Peter Schröder,et al.  Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing , 2003 .

[30]  Daniel Cohen-Or,et al.  Structure-aware shape processing , 2013, SIGGRAPH '14.

[31]  Thorsten Joachims,et al.  Semantic Labeling of 3D Point Clouds for Indoor Scenes , 2011, NIPS.

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

[33]  Aaron Hertzmann,et al.  Learning 3D mesh segmentation and labeling , 2010, SIGGRAPH 2010.

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

[35]  Thomas A. Funkhouser,et al.  Consistent segmentation of 3D models , 2009, Comput. Graph..

[36]  Yun Jiang,et al.  Hallucinated Humans as the Hidden Context for Labeling 3D Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Paolo Cignoni,et al.  MeshLab: an Open-Source 3D Mesh Processing System , 2008, ERCIM News.

[38]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Computing k-Nearest Neighbors , 1975, IEEE Transactions on Computers.

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

[40]  Ke Xie,et al.  A search-classify approach for cluttered indoor scene understanding , 2012, ACM Trans. Graph..

[41]  D. Cohen-Or,et al.  Style-content separation by anisotropic part scales , 2010, ACM Trans. Graph..