Selecting distinctive 3d shape descriptors for similarity retrieval
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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

Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval

Philip Shilane and Thomas Funkhouser


Large databases of 3d shapes

Computer Graphics Retrieval

(Princeton Shape Benchmark)

Mechanical CAD

(National Design Repository)

Molecular Biology

(Protein Databank)

Large Databases of 3D Shapes


Shape Retrieval Retrieval

3D Model

BestMatches

Model Database


Local matches for retrieval
Local Matches for Retrieval Retrieval

3D Model

BestMatches

Model Database


Local matches for retrieval1
Local Matches for Retrieval Retrieval

3D Model

BestMatches

Model Database

Cost Function


Local matches for retrieval2
Local Matches for Retrieval Retrieval

Using many local descriptors is slow.

3D Model

BestMatches

Model Database

Cost Function


Local matches for retrieval3
Local Matches for Retrieval 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 Retrieval

Focusing on the distinctive regions improves retrieval time and accuracy.

3D Model

BestMatches

Model Database

Cost Function


Related work

Selecting Local Descriptors Retrieval

RandomMori 2001Frome 2004

Related Work


Related work1

Selecting Local Descriptors Retrieval

Random

SalientGal 2005Lee 2005Frintrop 2004

Related Work


Related work2
Related Work Retrieval

Selecting Local Descriptors

  • Random

  • Salient

  • Likelihood Johnson 2000 Shan 2004


Distinction retrieval performance
Distinction = Retrieval Performance Retrieval

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 Retrieval

The distinct descriptors that distinguish between classes are classification dependent.

QueryDescriptors

Retrieval Results


Approach
Approach Retrieval

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

Descriptors

Distinction


Approach1
Approach Retrieval

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

Distinction

Distinction

Descriptors

1D Parameterization


Approach2
Approach Retrieval

Likelihood Parameterization

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

Distinction

Descriptors


System overview
System Overview Retrieval

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 Retrieval

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 Retrieval

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 Retrieval

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 Retrieval

Multi-dimensional normal density [Johnson 2000]


Likelihood of descriptors1
Likelihood of Descriptors Retrieval

The likelihood function is proportional to the descriptor density.


Map from descriptors to likelihood
Map from Descriptors to Likelihood Retrieval

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

More Likely

Less Likely


System overview4
System Overview Retrieval

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 Retrieval

Evaluate the retrieval performance of every query descriptor.

0.33

QueryDescriptors

Evaluation Metric

Retrieval Results


Measuring distinction1
Measuring Distinction Retrieval

Some descriptors are better for retrieval than others.

0.33

1.0

QueryDescriptors

Evaluation Metric

Retrieval Results


System overview5
System Overview Retrieval

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 Retrieval

Measure likelihood and retrieval performance of each descriptor.


Build distinction function1
Build Distinction Function Retrieval

Measure likelihood and retrieval performance of each descriptor.


Build distinction function2
Build Distinction Function Retrieval

Measure likelihood and retrieval performance of each descriptor.


Build distinction function3
Build Distinction Function Retrieval

Retrieval performance is averaged within each likelihood bin.


Descriptor distinction
Descriptor Distinction Retrieval

A likelihood mapping separates descriptors with different retrieval performance.

More Likely

Less Likely


Descriptor distinction1
Descriptor Distinction Retrieval

The most common features are the worst for retrieval.

More Likely

Less Likely


Predicting distinction
Predicting Distinction Retrieval

The likelihood mapping predicts descriptor distinction.

Descriptors

Distinction

Distinction Function


System overview6
System Overview Retrieval

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 Retrieval

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 Retrieval

3D Model

BestMatches

Model Database


Results
Results Retrieval

  • Examples of Distinctive Descriptors

  • Evaluation for Retrieval


Distinctive descriptor examples
Distinctive Descriptor Examples Retrieval

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


Distinctive descriptor examples1
Distinctive Descriptor Examples Retrieval

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


Challenge
Challenge Retrieval

The wheels are consistent features for cars.


Shape database
Shape Database Retrieval

  • 100 Models in 10 Classes from the Princeton Shape Benchmark

  • Models come from different branchesof the hierarchical classification


Shape descriptors

Radius of Descriptors Considered Retrieval

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 Retrieval

Using local descriptors improves retrieval relative to global descriptors.


Focus on distinctive descriptors
Focus on Distinctive Descriptors Retrieval

Using a small number of distinct descriptors maintains retrieval performance while improving retrieval time.




Alternative selection techniques2
Alternative Selection Techniques Retrieval

Distinction improves retrieval more than other techniques.


Conclusion
Conclusion Retrieval

  • 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 Retrieval

  • Explore other definitions of likelihood including mixture models


Future work1
Future Work Retrieval

  • Explore other definitions of likelihood including mixture models

  • Consider non-likelihood parameterizations


Future work2
Future Work Retrieval

  • Explore other definitions of likelihood including mixture models

  • Consider non-likelihood parameterizations

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


Acknowledgements
Acknowledgements Retrieval

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


The End Retrieval


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