geometric registration for deformable shapesresources.mpi-inf.mpg.de/deformableshapematching... ·...
Post on 22-Jul-2020
2 Views
Preview:
TRANSCRIPT
-
Geometric Registration for Deformable Shapes
2.2 Deformable RegistrationVariational Model· Deformable ICP
-
Variational ModelWhat is deformable shape matching?
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 33
Example
?
What are the Correspondences?
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 4
What are we looking for?
Problem Statement:
Given:• Two surfaces S1, S2 ⊆ ℝ3
We are looking for:• A reasonable deformation function f: S1 → ℝ3
that brings S1 close to S2
?
S1S2
f
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 55
Example
?
Correspondences? no shape match
too much deformation optimum
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 6
This is a Trade-Off
Deformable Shape Matching is a Trade-Off:• We can match any two shapes
using a weird deformation field
• We need to trade-off: Shape matching (close to data) Regularity of the deformation field (reasonable match)
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 77
Components:
Matching Distance:
Deformation / rigidity:
Variational Model
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 8
Variational Model
Variational Problem:• Formulate as an energy minimization problem:
)()()( )()( fEfEfE rregularizematch +=
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 9
Part 1: Shape Matching
Assume:• Objective Function:
• Example: least squares distance
• Other distance measures:Hausdorf distance, Lp-distances, etc.
• L2 measure is frequently used (models Gaussian noise)
S2f(S1)
( )212,1)( ),()( SSfdistfE match =
∫∈
=11
12
21)( ),()(
Sx
match dSdistfE xx
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 10
Point Cloud Matching
Implementation example: Scan matching• Given: S1, S2 as point clouds
S1 = {s1(1), …, sn(1)} S2 = {s1(2), …, sm(2)}
• Energy function:
• How to measure ?
Estimate distance to a point sampled surface
si(2)
fi(S1)( )∑
=
=m
ii
match SdistmSfE
1
2)2(1
1)( ,||)( s
( )x,1Sdist
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 11
Surface approximation
Solution #1: Closest point matching• “Point-to-point” energy
( )∑=
=m
iiSini
match sNNsdistmSfE
1
2)2()2(1)( )(,||)(1
si(2)
f(S1)
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 12
Surface approximation
Solution #2: Linear approximation• “Point-to-plane” energy• Fit plane to k-nearest neighbors• k proportional to noise level, typically k ≈ 6…20
si(2)
f(S1)
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 13
Surface approximation
Solution #3: Higher order approximation• Higher order fitting (e.g. quadratic)
Moving least squares
si(2)
f(S1)
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 14
Variational Model
Variational Problem:• Formulate as an energy minimization problem:
)()()( )()( fEfEfE rregularizematch +=
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 15
What is a “nice” deformation field?• Isometric “elastic” energies
Extrinsic (“volumetric deformation”) Intrinsic (“as-isometric-as
possible embedding”)
• Thin shell model Preserves shape (metric plus curvature)
• Thin-plate splines Allowing strong deformations, but keep shape
Part II: Deformation Model
)()( fE rregularize
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 16
Elastic Volume Model
Extrinsic Volumetric “As-Rigid-As Possible”• Embed source surface S1 in volume• f should preserve 3×3 metric tensor (least squares)
[ ]∫ −∇∇=1
2T)( )(V
rregularize dxfffE Ifirst fundamental form I (ℝ3×3)
S1
V1f
S2
∇f
f (V1)ambient space
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 17
Volume Model
Variant: Thin-Plate-Splines• Use regularizer that penalizes curved deformation
second derivative (ℝ3×3)
S1
V1f
S2
Hf =∇(∇f )
f (V1)ambient space
∫=1
2)( )()(V
frregularize dxxHfE
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
How does the deformation look like?
original
as-rigid-aspossiblevolume
thinplate
splines
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
Intrinsic Matching (2-Manifold)• Target shape is given and complete• Isometric embedding
[ ]∫ −∇∇=1
2T)( )(S
rregularize dxfffE Ifirst fund. form (S1, intrinsic)
19
Isometric Regularizer
19
S1
f
S2
∇f
tangent space
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
“Thin Shell” Energy• Differential geometry point of view
Preserve first fundamental form I Preserve second fundamental form II …in a least least squares sense
• Complicated to implement• Usually approximated
Volumetric shells (as shown before) Other approximation (next slide)
20
Elastic “Thin Shell” Regularizer
20
S1
S2
f
III
III
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
Example Implementation
Example: approximate thin shell model• Keep locally rigid
Will preserve metric & curvature implicitly
• Idea Associate local rigid transformation with surface points Keep as similar as possible Optimize simultaneously with deformed surface
• Transformation is implicitly defined by deformed surface (and vice versa)
21
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 22
Parameterization
Parameterization of S1• Surfel graph • This could be a mesh, but does not need to
edges encodetopology
surfel graph
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 23
Deformation
Ai
Orthonormal Matrix Aiper surfel (neighborhood),latent variable
Ai
predictionframe t frame t+1
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 24
Deformation
Ai
Orthonormal Matrix Aiper surfel (neighborhood),latent variable
Ai
prediction
error
frame t frame t+1
( ) ( )[ ]2)1()1()()()( ∑ ∑ ++ −−−=surfels neighbors
ti
ti
ti
ti
ti
rregularizejj
E ssssA
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 25
Unconstrained Optimization
Orthonormal matrices• Local, 1st order, non-degenerate parametrization:
• Optimize parameters α, β, γ, then recompute A0• Compute initial estimate using [Horn 87 ]
−−−=
00
0)(
γβγαβα
ti×C
)(
)exp()(
0
0
ti
ii
I ×
×
CA
CAA
+⋅=
=
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes 26
Variational Model
Variational Problem:• Formulate as an energy minimization problem:
)()()( )()( fEfEfE rregularizematch +=
-
Deformable ICP
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
Deformable ICP
How to build a deformable ICP algorithm• Pick a surface distance measure• Pick an deformation model / regularizer
2828
)()()( )()( fEfEfE rregularizematch +=
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
Deformable ICP
How to build a deformable ICP algorithm• Pick a surface distance measure• Pick an deformation model / regularizer• Initialize f(S1) with S1 (i.e., f = id)• Pick a non-linear optimization algorithm
Gradient decent (easy, but bad performance) Preconditioned conjugate gradients (better) Newton or Gauss Newton (recommended, but more work) Always use analytical derivatives!
• Run optimization
-
Eurographics 2010 Course – Geometric Registration for Deformable Shapes
Example
Example• Elastic model• Local rigid coordinate
frames• Align A→B, B→A
30
Geometric Registration for Deformable ShapesVariational Model�What is deformable shape matching?ExampleWhat are we looking for?ExampleThis is a Trade-OffVariational ModelVariational ModelPart 1: Shape MatchingPoint Cloud MatchingSurface approximationSurface approximationSurface approximationVariational ModelPart II: Deformation ModelElastic Volume ModelVolume ModelHow does the deformation look like?Isometric RegularizerElastic “Thin Shell” RegularizerExample ImplementationParameterizationDeformationDeformationUnconstrained OptimizationVariational ModelDeformable ICPDeformable ICPDeformable ICPExample
top related