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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

Indexing with Unknown Illumination and Pose

Ira Kemelmacher Ronen Basri

Weizmann Institute of Science

the task shape indexing

Hippo

Dino

Camel

The task – Shape Indexing

1. Recognize

2. Recover pose+lighting

why is it hard
Why is it hard?
  • Unknown pose
  • Unknown lighting
  • Clutter
  • Occlusion
assumptions
Assumptions
  • Weak perspective projection
  • 3D rigid transformation
  • Lambertian model
previous
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
Previous Indexing Methods
  • Ignored intensity information
  • Need many point or line features
  • Restricted to polyhedral objects
our algorithm
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

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

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

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

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
False Matches

True match

how to eliminate the false matches
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
Harmonic Images – Linear Basis for Lighting(Basri and Jacobs ‘01, Ramamoorthi and Hanrahan ‘01)

r

representation by harmonics
Representation by harmonics

Unknown light

I = b * H

Intensities of image point set

Harmonics of model point set

the consistency measure
The consistency measure

I = b * H

For corresponding image and model sets this is minimal

Should we apply it on feature points?

smooth points
“Smooth points”

Smooth points

Feature points

voting
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
Experiments
  • Real 3d objects acquired using laser scanner
  • Feature points collected automatically using Harris corner detector
results

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results
results21

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results
results indoor scene

dino

hippo

camel

shark

pinokio

bear

elephant

face

Results – Indoor scene
results outdoor scene

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Outdoor scene
results night scene

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Night Scene
results night scene 2

dino

camel

shark

pinokio

bear

elephant

face

hippo

Results – Night Scene 2
how much does lighting help
How much does lighting help?

Voting based on Affine model

how much does lighting help29
How much does lighting help?

Affine + Rigidity test

how much does lighting help30
How much does lighting help?

Affine + Rigidity + Lighting

conclusion
Conclusion
  • Identify 3d objects in 2d scenes
    • Unknown pose, light
    • Clutter, occlusions
    • General, real objects
    • Fast, efficient
  • Combination of intensity cues and geometry