Eurographics Symposium on Geometry Processing 2013 Dynamic Maps for Exploring and Browsing Shapes

Large datasets of 3D objects require an intuitive way to browse and quickly explore shapes from the collection. We present a dynamic map of shapes where similar shapes are placed next to each other. Similarity between 3D models exists in a high dimensional space which cannot be accurately expressed in a two dimensional map. We solve this discrepancy by providing a local map with pan capabilities and a user interface that resembles an online experience of navigating through geographical maps. As the user navigates through the map, new shapes appear which correspond to the specific navigation tendencies and interests of the user, while maintaining a continuous browsing experience. In contrast with state of the art methods which typically reduce the search space by selecting constraints or employing relevance feedback, our method enables exploration of large sets without constraining the search space, allowing the user greater creativity and serendipity. A user study evaluation showed a strong preference of users for our method over a standard relevance feedback method.

[1]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

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

[3]  Leonidas J. Guibas,et al.  Exploration of continuous variability in collections of 3D shapes , 2011, ACM Trans. Graph..

[4]  Daniel A. Keim,et al.  Automatic selection and combination of descriptors for effective 3D similarity search , 2004, IEEE Sixth International Symposium on Multimedia Software Engineering.

[5]  Tsuhan Chen,et al.  Efficient feature extraction for 2D/3D objects in mesh representation , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[6]  Daniel Cohen-Or,et al.  Image Appearance Exploration by Model‐Based Navigation , 2009, Comput. Graph. Forum.

[7]  Luiz Velho,et al.  Learning good views through intelligent galleries , 2009, Comput. Graph. Forum.

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

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

[10]  Ryutarou Ohbuchi,et al.  SHREC'10 Track: Generic 3D Warehouse , 2010, 3DOR@Eurographics.

[11]  Ron Meir,et al.  Semantic-oriented 3d shape retrieval using relevance feedback , 2005, The Visual Computer.

[12]  Szymon Rusinkiewicz,et al.  Symmetry descriptors and 3D shape matching , 2004, SGP '04.

[13]  Michael Elad,et al.  Content Based Retrieval of VRML Objects - An Iterative and Interactive Approach , 2001, Eurographics Multimedia Workshop.

[14]  Michael Elad,et al.  Content based retrieval of VRML objects: an iterative and interactive approach , 2002 .

[15]  Mounia Lalmas,et al.  A survey on the use of relevance feedback for information access systems , 2003, The Knowledge Engineering Review.

[16]  Bülent Sankur,et al.  Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization , 2010, International Journal of Computer Vision.

[17]  Daniel Cohen-Or,et al.  Active co-analysis of a set of shapes , 2012, ACM Trans. Graph..

[18]  Cohen-OrDaniel,et al.  Consistent mesh partitioning and skeletonisation using the shape diameter function , 2008 .

[19]  Yasuhiko Sakamoto,et al.  Motion map: image-based retrieval and segmentation of motion data , 2004, SCA '04.

[20]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[21]  Pat Hanrahan,et al.  Exploratory modeling with collaborative design spaces , 2009, ACM Trans. Graph..

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

[23]  Marcin Novotni,et al.  3D zernike descriptors for content based shape retrieval , 2003, SM '03.

[24]  Remco C. Veltkamp,et al.  A Survey of Content Based 3D Shape Retrieval Methods , 2004, SMI.

[25]  Helmut Pottmann,et al.  Shape space exploration of constrained meshes , 2011, ACM Trans. Graph..

[26]  Pat Hanrahan,et al.  Exploratory modeling with collaborative design spaces , 2009, SIGGRAPH 2009.

[27]  Sylvain Lefebvre,et al.  Procedural texture preview , 2012, Comput. Graph. Forum.

[28]  Siddhartha Chaudhuri,et al.  Data-driven suggestions for creativity support in 3D modeling , 2010, ACM Trans. Graph..

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

[30]  Remco C. Veltkamp,et al.  A survey of content based 3D shape retrieval methods , 2004, Proceedings Shape Modeling Applications, 2004..

[31]  Takeo Igarashi,et al.  Guided exploration of physically valid shapes for furniture design , 2012, ACM Trans. Graph..

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

[33]  W. Bruce Croft,et al.  Relevance Feedback and Personalization: A Language Modeling Perspective , 2001, DELOS.

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

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

[36]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[37]  H. Seidel,et al.  Pattern-aware Deformation Using Sliding Dockers , 2011, SIGGRAPH 2011.

[38]  Indriyati Atmosukarto,et al.  Feature Combination and Relevance Feedback for 3D Model Retrieval , 2005, 11th International Multimedia Modelling Conference.

[39]  Liangliang Cao,et al.  3D object retrieval using 2D line drawing and graph based relevance reedback , 2006, MM '06.

[40]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

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