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Understanding shapes Fun with shapes

Understanding shapes Fun with shapes. Li Guo 2011.07.04. Exploration of Continuous Variability in Collections of 3D Shapes (Sig11) Characterizing Structural Relationships in Scenes Using Graph Kernels  (Sig11) Context-Based Search for 3D Models (SigA10)

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Understanding shapes Fun with shapes

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  1. Understanding shapesFun with shapes Li Guo 2011.07.04

  2. Exploration of Continuous Variability in Collections of 3D Shapes (Sig11) • Characterizing Structural Relationships in Scenes Using Graph Kernels  (Sig11) • Context-Based Search for 3D Models (SigA10) • Shape google: Geometric words and expressions for invariant shape retrieval (TOG11) • Making Burr Puzzles from 3D Models (Sig11) • A Geometric Study of V-style Pop-ups: Theories and Algorithms (Sig11) • Depixelizing Pixel Art (Sig11) • Digital Micrography (Sig11)

  3. Exploration of Continuous Variability in Collections of 3D Shapes

  4. Authors

  5. What(Video) • Propose a new technique for exploring unorganized collections of 3D models

  6. Motivation • 3D models become more and more • Text-based search • Many within class • Navigating directly in descriptor space • High-dimensional • Not intuitive • Example-based retrieval

  7. Related work • Morphable models and deformation modeling • Global correspondence detection remains a challenging open problem • Exploring shape datasets • Text keywords • Proxies • Example-based search

  8. Selling points • We present a template-based interface for exploring collections of similar 3D models via constrained direct manipulation. • We introduce a novel technique to convert descriptor variability into a deformation model for a template shape without relying on correspondences between shapes.

  9. Overview

  10. Descriptor variability and template deformations Shape descriptor Shape PCA basis Deformation Space Template deformation PCA basis

  11. Shape descriptor

  12. Template selection and deformation space • Template selection • Order the shapes by the distance to the average descriptor • Filter the shapes have many components • Deformation space • Template shape with C components • 6C deformation parameters(3 translation and 3 scaling)

  13. Exploration interface

  14. Results

  15. Future work • An explicit encoding of the part connectivity • A convex formulation of a similar optimization problem • Outlier detection for shape retrieval • Analyzing the relation of discrete variability in the shape • Extensions to our exploration interface

  16. Characterizing Structural Relationships in Scenes Using Graph Kernels

  17. Authors ?

  18. What • Represent scenes as graphs that encode models and their semantic relationships • Applications • Finding similar scenes • Relevance feedback • Context-based model search

  19. Motivation • Scene comparison

  20. Related work • 3D Model Search • Scene Comparison • [Harchaoui and Bach 2007] Image comparison

  21. Spatial Relationships

  22. Representing Scenes As Graphs • Enclosure, Horizontal Support, Vertical Contact, Oblique Contact

  23. Graph Comparison • Node Kernel • Edge Kernel • Graph Kernel: [Harchaoui and Bach 2007] • Embedding the graphs in a very high dimensional feature space and computing an inner product

  24. Dataset • Google 3D Warehouse • Most have scene graph • Standardize the tagging and segmentation (mimics the method such as PASCAL,MSRC, and LabelMe [Russell et al. 2008]

  25. Application:Relevance feedback

  26. Find Similar Scene

  27. Context-based model search

  28. Comparison

  29. Limitations • Simple relationship • Many scenes were not reasonably segmented

  30. Future work • Software that is aware of the relationships expressed in 3D scenes has significant potential to augment the scene design process.

  31. Context-Based Search for 3D Models

  32. Authors

  33. What • Context search

  34. Motivation • 3D model search • Scene modeling • The goal of this research is to develop a context-based 3D search engine

  35. Related work • Geometric Search Engines • Spatial Context in Computer Vision • The context challenge

  36. Dataset • Google 3D Warehouse • Most have scene graph • Standardize the tagging and segmentation (mimics the method such as PASCAL,MSRC, and LabelMe [Russell et al. 2008]

  37. Overview • Observations • All pairs of object co-occurrence across all scenes • Spatial Relationships • Object Similarity • Model Ranking

  38. Results

  39. Benefit of additional supporting objects

  40. Comparing results with and without database tags

  41. Failure Cases • Geometrically very similar to a relevant object but semantically very different • Spatial relationships are overly simplistic

  42. Future work • Extracting more meaningful spatial relationships between objects • Intelligently perform complex actions(意识流)

  43. Shape Google: geometric words and expressions for invariant shape retrieval

  44. Authors LEONIDAS J. GUIBAS MAKS OVSJANIKOV Alex M. Bronstein Michael M. Bronstein

  45. What • Non-rigid shape search and retrieval

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