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Indexing with Unknown Illumination and Pose. Ira Kemelmacher Ronen Basri Weizmann Institute of Science. …. Hippo. Dino. Camel. The task – Shape Indexing. 1. Recognize. 2. Recover pose+lighting. Why is it hard?. Unknown pose Unknown lighting. Clutter. Occlusion.

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Indexing with unknown illumination and pose l.jpg

Indexing with Unknown Illumination and Pose

Ira Kemelmacher Ronen Basri

Weizmann Institute of Science


The task shape indexing l.jpg

Hippo

Dino

Camel

The task – Shape Indexing

1. Recognize

2. Recover pose+lighting


Why is it hard l.jpg
Why is it hard?

  • Unknown pose

  • Unknown lighting

  • Clutter

  • Occlusion


Assumptions l.jpg
Assumptions

  • Weak perspective projection

  • 3D rigid transformation

  • Lambertian model


Previous l.jpg
Previous…

  • Identification using alignment

    Fischler and Bolles, Huttenlocher and Ullman

  • “3D to 2D invariants do not exist”

    Burns etal., Moses etal., Clemens etal.

  • Indexing faster than alignment Jacobs, Wolfson etal.


Previous indexing methods l.jpg
Previous Indexing Methods

  • Ignored intensity information

  • Need many point or line features

  • Restricted to polyhedral objects


Our algorithm l.jpg
Our algorithm…

  • Handles both pose and lighting

  • Uses intensities to filter out incorrect matches

  • Still relies on point features but only very few are needed

  • General objects


Indexing with pose affine model jacobs 96 l.jpg

p2

p3

(α3 ,β3)

P2

p4

P3

(α4 ,β4)

p0

P4

p1

P0

P1

Indexing with pose - Affine model (Jacobs ‘96)

pi = APi + t 8 DOF -> 5 points

(n1,n2,m)


Representation in two 2d tables l.jpg

p2

p3

(α3 ,β3)

P2

p4

P3

(α4 ,β4)

p0

P4

p1

(n1,n2,m)

P0

P1

Representation in two 2D tables

α -space

Offline –

preprocessing

Online –

matching

(α3,α4)

α4 = α3m + n1

β -space

β4 = β3m + n2

(β3, β4)


Modifications still two 2d spaces l.jpg

p2

p3

(α3 ,β3)

P2

p4

P3

(α4 ,β4)

p0

P4

p1

(n1,n2,m)

P0

P1

Modifications – still two 2D spaces

Offline –

preprocessing

Online –

matching

α –dual-space

(θ,r1)

r1= α3cos θ+ α4 sinθ

β –dual-space

(θ,r2)

r2= β3cos θ+ β4 sinθ


One 3d space l.jpg

p2

p3

(α3 ,β3)

P2

p4

P3

(α4 ,β4)

p0

P4

p1

(n1,n2,m)

P0

P1

One 3D space

Offline –

preprocessing

Online –

matching

(θ,r1,r2)


False matches l.jpg
False Matches

True match


How to eliminate the false matches l.jpg
How to eliminate the false matches

  • Enforce rigidity using inverse Gramian Test- Weinshall, ‘93

  • Consistency with lighting  NEXT


Harmonic images linear basis for lighting basri and jacobs 01 ramamoorthi and hanrahan 01 l.jpg
Harmonic Images – Linear Basis for Lighting(Basri and Jacobs ‘01, Ramamoorthi and Hanrahan ‘01)

r


Representation by harmonics l.jpg
Representation by harmonics

Unknown light

I = b * H

Intensities of image point set

Harmonics of model point set


The consistency measure l.jpg
The consistency measure

I = b * H

For corresponding image and model sets this is minimal

Should we apply it on feature points?


Smooth points l.jpg
“Smooth points”

Smooth points

Feature points


Voting l.jpg
Voting

  • Sets of points that pass the lighting test vote for their respective model

  • All models receive scores:

  • Score = fraction of image sets for which the model appears min

  • Once model is selected its corresponding subsets used to determine its pose and lighting


Experiments l.jpg
Experiments

  • Real 3d objects acquired using laser scanner

  • Feature points collected automatically using Harris corner detector


Results l.jpg

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results


Results21 l.jpg

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results



Results indoor scene l.jpg

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results – Indoor scene


Results outdoor scene l.jpg

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Outdoor scene


Results night scene l.jpg

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Night Scene


Results night scene 2 l.jpg

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Night Scene 2



How much does lighting help l.jpg
How much does lighting help?

Voting based on Affine model


How much does lighting help29 l.jpg
How much does lighting help?

Affine + Rigidity test


How much does lighting help30 l.jpg
How much does lighting help?

Affine + Rigidity + Lighting


Conclusion l.jpg
Conclusion

  • Identify 3d objects in 2d scenes

    • Unknown pose, light

    • Clutter, occlusions

    • General, real objects

    • Fast, efficient

  • Combination of intensity cues and geometry