Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval - PowerPoint PPT Presentation

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Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

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  1. Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval Philip Shilane and Thomas Funkhouser

  2. Computer Graphics (Princeton Shape Benchmark) Mechanical CAD (National Design Repository) Molecular Biology (Protein Databank) Large Databases of 3D Shapes

  3. Shape Retrieval 3D Model BestMatches Model Database

  4. Local Matches for Retrieval 3D Model BestMatches Model Database

  5. Local Matches for Retrieval 3D Model BestMatches Model Database Cost Function

  6. Local Matches for Retrieval Using many local descriptors is slow. 3D Model BestMatches Model Database Cost Function

  7. Local Matches for Retrieval Using many local descriptors is slow. Many descriptors do not represent distinguishing parts. 3D Model BestMatches Model Database Cost Function

  8. Local Matches for Retrieval Focusing on the distinctive regions improves retrieval time and accuracy. 3D Model BestMatches Model Database Cost Function

  9. Selecting Local Descriptors RandomMori 2001Frome 2004 Related Work

  10. Selecting Local Descriptors Random SalientGal 2005Lee 2005Frintrop 2004 Related Work

  11. Related Work Selecting Local Descriptors • Random • Salient • Likelihood Johnson 2000 Shan 2004

  12. Distinction = Retrieval Performance The distinction of each local descriptor is based on how well it retrieves shapes of the correct class. QueryDescriptors Retrieval Results

  13. Distinction = Retrieval Performance The distinct descriptors that distinguish between classes are classification dependent. QueryDescriptors Retrieval Results

  14. Approach We want a predicted distinction score for each descriptor on the model. Descriptors Distinction

  15. Approach We map descriptors into a 1D space where we learn distinction from a training set. Distinction Distinction Descriptors 1D Parameterization

  16. Approach Likelihood Parameterization Likelihood of shape descriptors is a 1D function that groups descriptors with similar distinction. Distinction Descriptors

  17. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  18. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  19. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  20. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  21. Likelihood of Descriptors Multi-dimensional normal density [Johnson 2000]

  22. Likelihood of Descriptors The likelihood function is proportional to the descriptor density.

  23. Map from Descriptors to Likelihood Flat regions are the most common while wing tips and the cockpit area are rarer. More Likely Less Likely

  24. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  25. Measuring Distinction Evaluate the retrieval performance of every query descriptor. 0.33 QueryDescriptors Evaluation Metric Retrieval Results

  26. Measuring Distinction Some descriptors are better for retrieval than others. 0.33 1.0 QueryDescriptors Evaluation Metric Retrieval Results

  27. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  28. Build Distinction Function Measure likelihood and retrieval performance of each descriptor.

  29. Build Distinction Function Measure likelihood and retrieval performance of each descriptor.

  30. Build Distinction Function Measure likelihood and retrieval performance of each descriptor.

  31. Build Distinction Function Retrieval performance is averaged within each likelihood bin.

  32. Descriptor Distinction A likelihood mapping separates descriptors with different retrieval performance. More Likely Less Likely

  33. Descriptor Distinction The most common features are the worst for retrieval. More Likely Less Likely

  34. Predicting Distinction The likelihood mapping predicts descriptor distinction. Descriptors Distinction Distinction Function

  35. System Overview Training Shape DB Local Descriptors Likelihood Descriptor DB Distinction Function Retrieval Evaluation Classification Query Local Descriptors Likelihood Evaluate Distinction SelectDescriptors Match Shape RetrievalList

  36. Selecting Distinctive Descriptors The k most distinctive descriptors with a minimum distance constraint are selected. Mesh Descriptors DistinctionScores 3 SelectedDescriptors

  37. Matching with Selected Descriptors 3D Model BestMatches Model Database

  38. Results • Examples of Distinctive Descriptors • Evaluation for Retrieval

  39. Distinctive Descriptor Examples Descriptors on the head and neck represent consistent regions of the models.

  40. Distinctive Descriptor Examples Descriptors on the front of the jet are consistent as opposed to on the wings.

  41. Challenge The wheels are consistent features for cars.

  42. Shape Database • 100 Models in 10 Classes from the Princeton Shape Benchmark • Models come from different branchesof the hierarchical classification

  43. Radius of Descriptors Considered 0.25 0.5 1.0 2.0 Shape Descriptors • Mass per Shell Shape Histogram (SHELLS) Ankerst 1999 • Spherical Harmonics of the Gaussian Euclidean Distance Transform (SHD) Kazhdan 2003

  44. Local vs. Global Descriptors Using local descriptors improves retrieval relative to global descriptors.

  45. Focus on Distinctive Descriptors Using a small number of distinct descriptors maintains retrieval performance while improving retrieval time.

  46. Alternative Selection Techniques

  47. Alternative Selection Techniques

  48. Alternative Selection Techniques Distinction improves retrieval more than other techniques.

  49. Conclusion • Method to select distinctive descriptors • Distinctive descriptors can improve retrieval • Mapping descriptors through likelihood and learned retrieval performance to distinction is better than other alternatives • Distinction is independent of type of descriptor

  50. Future Work • Explore other definitions of likelihood including mixture models