Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

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

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

Shape Retrieval 3D Model BestMatches Model Database

Local Matches for Retrieval 3D Model BestMatches Model Database

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

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

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

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

Selecting Local Descriptors RandomMori 2001Frome 2004 Related Work

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Matching with Selected Descriptors 3D Model BestMatches Model Database

Results • Examples of Distinctive Descriptors • Evaluation for Retrieval

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

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

Challenge The wheels are consistent features for cars.

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

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

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

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

Alternative Selection Techniques

Alternative Selection Techniques Distinction improves retrieval more than other techniques.

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

Future Work • Explore other definitions of likelihood including mixture models

Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval