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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

(Princeton Shape Benchmark)

Mechanical CAD

(National Design Repository)

Molecular Biology

(Protein Databank)

Large Databases of 3D Shapes
slide3

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 work1
Selecting Local Descriptors

Random

SalientGal 2005Lee 2005Frintrop 2004

Related Work
related work2
Related Work

Selecting Local Descriptors

  • Random
  • Salient
  • Likelihood Johnson 2000 Shan 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 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