Selecting distinctive 3d shape descriptors for similarity retrieval
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Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval PowerPoint PPT Presentation

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.

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Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

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Selecting distinctive 3d shape descriptors for similarity retrieval

Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

Philip Shilane and Thomas Funkhouser


Large databases of 3d shapes

Computer Graphics

(Princeton Shape Benchmark)

Mechanical CAD

(National Design Repository)

Molecular Biology

(Protein Databank)

Large Databases of 3D Shapes


Shilane smi06

Shape Retrieval

3D Model

BestMatches

Model Database


Local matches for retrieval

Local Matches for Retrieval

3D Model

BestMatches

Model Database


Local matches for retrieval1

Local Matches for Retrieval

3D Model

BestMatches

Model Database

Cost Function


Local matches for retrieval2

Local Matches for Retrieval

Using many local descriptors is slow.

3D Model

BestMatches

Model Database

Cost Function


Local matches for retrieval3

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 retrieval4

Local Matches for Retrieval

Focusing on the distinctive regions improves retrieval time and accuracy.

3D Model

BestMatches

Model Database

Cost Function


Related work

Selecting Local Descriptors

RandomMori 2001Frome 2004

Related Work


Related work1

Selecting Local Descriptors

Random

SalientGal 2005Lee 2005Frintrop 2004

Related Work


Related work2

Related Work

Selecting Local Descriptors

  • Random

  • Salient

  • LikelihoodJohnson 2000Shan 2004


Distinction retrieval performance

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 performance1

Distinction = Retrieval Performance

The distinct descriptors that distinguish between classes are classification dependent.

QueryDescriptors

Retrieval Results


Approach

Approach

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

Descriptors

Distinction


Approach1

Approach

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

Distinction

Distinction

Descriptors

1D Parameterization


Approach2

Approach

Likelihood Parameterization

Likelihood of shape descriptors is a 1D function that groups descriptors with similar distinction.

Distinction

Descriptors


System overview

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


System overview1

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


System overview2

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


System overview3

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

Likelihood of Descriptors

Multi-dimensional normal density [Johnson 2000]


Likelihood of descriptors1

Likelihood of Descriptors

The likelihood function is proportional to the descriptor density.


Map from descriptors to likelihood

Map from Descriptors to Likelihood

Flat regions are the most common while wing tips and the cockpit area are rarer.

More Likely

Less Likely


System overview4

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


Measuring distinction

Measuring Distinction

Evaluate the retrieval performance of every query descriptor.

0.33

QueryDescriptors

Evaluation Metric

Retrieval Results


Measuring distinction1

Measuring Distinction

Some descriptors are better for retrieval than others.

0.33

1.0

QueryDescriptors

Evaluation Metric

Retrieval Results


System overview5

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


Build distinction function

Build Distinction Function

Measure likelihood and retrieval performance of each descriptor.


Build distinction function1

Build Distinction Function

Measure likelihood and retrieval performance of each descriptor.


Build distinction function2

Build Distinction Function

Measure likelihood and retrieval performance of each descriptor.


Build distinction function3

Build Distinction Function

Retrieval performance is averaged within each likelihood bin.


Descriptor distinction

Descriptor Distinction

A likelihood mapping separates descriptors with different retrieval performance.

More Likely

Less Likely


Descriptor distinction1

Descriptor Distinction

The most common features are the worst for retrieval.

More Likely

Less Likely


Predicting distinction

Predicting Distinction

The likelihood mapping predicts descriptor distinction.

Descriptors

Distinction

Distinction Function


System overview6

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


Selecting distinctive descriptors

Selecting Distinctive Descriptors

The k most distinctive descriptors with a minimum distance constraint are selected.

Mesh

Descriptors

DistinctionScores

3 SelectedDescriptors


Matching with selected descriptors

Matching with Selected Descriptors

3D Model

BestMatches

Model Database


Results

Results

  • Examples of Distinctive Descriptors

  • Evaluation for Retrieval


Distinctive descriptor examples

Distinctive Descriptor Examples

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


Distinctive descriptor examples1

Distinctive Descriptor Examples

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


Challenge

Challenge

The wheels are consistent features for cars.


Shape database

Shape Database

  • 100 Models in 10 Classes from the Princeton Shape Benchmark

  • Models come from different branchesof the hierarchical classification


Shape descriptors

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

Local vs. Global Descriptors

Using local descriptors improves retrieval relative to global descriptors.


Focus on distinctive 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


Alternative selection techniques1

Alternative Selection Techniques


Alternative selection techniques2

Alternative Selection Techniques

Distinction improves retrieval more than other techniques.


Conclusion

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

Future Work

  • Explore other definitions of likelihood including mixture models


Future work1

Future Work

  • Explore other definitions of likelihood including mixture models

  • Consider non-likelihood parameterizations


Future work2

Future Work

  • Explore other definitions of likelihood including mixture models

  • Consider non-likelihood parameterizations

  • Combine descriptors while accounting for deformation [Funkhouser and Shilane, SGP]


Acknowledgements

Acknowledgements

Szymon Rusinkiewicz

Joshua Podolak

Princeton Graphics Group

Funding Sources:

National Science Foundation Grant CCR-0093343 and Grant 11S-0121446

Air Force Research Laboratory Grant FA8650-04-1-1718


Shilane smi06

The End


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