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CK1 Intelligent Surface Modeler. By Yu Wing TAI Kam Lun TANG Advised by Prof. Chi Keung TANG. Overview of presentation. Motivation Tensor Voting Algorithm Implementation Results Conclusion. Motivation Tensor Voting Algorithm Implementation Results Conclusion.

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Ck1 intelligent surface modeler

CK1Intelligent Surface Modeler

By

Yu Wing TAI

Kam Lun TANG

Advised by

Prof. Chi Keung TANG

2002-2003 FYP Presentataion


Overview of presentation
Overview of presentation

  • Motivation

  • Tensor Voting Algorithm

  • Implementation

  • Results

  • Conclusion

  • Motivation

  • Tensor Voting Algorithm

  • Implementation

  • Results

  • Conclusion

  • theory and applications

  • tensor and voting

  • data representation and communication

  • bunny

  • dragon

  • etc

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Motivation
Motivation

  • Theoretical interest

    • Emulate human visual perception

  • Applications

    • 3D modeling

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Overview of presentation1
Overview of presentation

  • Motivation

  • Tensor Voting Algorithm

  • Implementation

  • Results

  • Conclusion

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


What is tensor voting
What is Tensor Voting?

  • Representation

  • Constraint propagation

  • Data communication

TENSOR

VOTING FIELDS

VOTING ALGORITHM

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Tensor ellipse

+

Tensor = Ellipse

SMOOTH CURVE

POINT JUNCTION

=

ELLIPSE (TENSOR)

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Tensor ellipse1

-

stick tensor

ball tensor

2D tensor

Tensor = Ellipse

-

  • Ball Tensor - 100% uncertainty in all directions

  • Stick Tensor - 100% certainty in normal directions

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


2d stick voting field
2D Stick Voting Field

  • Encode smoothness

?

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


2d ball voting field
2D Ball Voting Field

  • Derived from 2D stick voting field

    • Rotation and integration

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Voting algorithm

+

=

+

=

+

=

+

=

Voting Algorithm

Each input site propagates its information in a neighborhood

voting = summation of tensor votes accumulated in a neighborhood

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


3d tensor voting

points

curvels

surfels

Encode

balls

plates

sticks

Sparse Tensor Voting

ball voting field

plate voting field

stick voting field

tensor tokens

dense tensor map

Dense Tensor Voting

surface saliency map

surface

Decompose

Feature Extraction

3D Tensor Voting

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Results
Results

  • Noisy data

  • Sparse data

  • Large scale reconstruction

  • Efficient neighborhood searching in 3D space

  • Code Optimization

  • Qualitative and quantitative analysis

  • Noisy data

  • Sparse data

  • Large scale reconstruction

  • Efficient neighborhood searching in 3D space

  • Code Optimization

  • Qualitative and quantitative analysis

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Result robustness

100% noise

300% noise

500% noise

1000% noise

1500% noise

2000% noise

3000% noise

5000% noise

Result: Robustness

2002-2003 FYP Presentataion


Large scale reconstruction
Large scale reconstruction

1,153,856 triangles

35974 points

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Conclusion
Conclusion

  • Intelligent surface modeler

    • 3D surface description

  • Tensor voting

  • Results

    • Robustness

    • Large scale reconstruction

  • Future work

    • Multiscale feature segmentation and extraction

Presented by:

Yu Wing TAI

Kam Lun TANG

2002-2003 FYP Presentataion


Thank you

Q&A

2002-2003 FYP Presentataion


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